Abstract: A great challenge has posed on the trans-regional operation of agricultural machinery, including many tasks, strong timeliness and relatively fixed operational sequence. Therefore, a scientific and reasonable deployment scheme is still lacking to efficiently maintain operations for the production tracking in agricultural machinery in recent years. This study aims to optimize the cooperative scheduling for the multi-task and multi-machine assignment in a large-scale farm using improved multi-parent genetic algorithm (GA). Firstly, the basic locations of farmland and agricultural machinery were easily acquired and stored, as well as the task requirements distributed by farmers, with the aid of agricultural Internet of Things (IoTs) and navigation system. A multi-machine coupled with multi-tasks was analyzed, where the actual continuous operation was required to be completed in a specified time on a farm, for example, the tillage, sowing, and fertilization. Some parameters were also initialized, including the operation sequence, and the number of agricultural machines. A scheduling model was then built using a time window under the boundary conditions of multi-type machines, the distance of operation deployment, the time of preparation and operation. Besides, the model must also satisfy the following basic rules: 1) Each machine can only work on one field at the same time; 2) each field can be arranged with multiple agricultural machineries of the same task if the agricultural machineries are sufficient; 3) the operation sequence of different tasks in each farmland is fixed for the special operational procedures, while the tasks in each farmland must be executed. After that, taking the minimum operation time as the optimization goal, a feasible scheduling system was proposed using the improved multi-parent genetic algorithm (IMPGA) for the task planning. The field ID was used as the gene in the process of encoding, while the frequency of ID in the chromosome corresponded to the task of field, as the task process cannot be changed. Two parts were divided after generating primary population, remarked as the excellent and good group. The population propagates were then used in the strategy of multi-parent crossover, where a relatively superior individual was chosen from the excellent group to intersect with two good individuals chosen from the good one. The mutation probability was designed to be adjustable, when the fitness of optimal chromosome in the population cannot change after several iterations. Finally, the performance of Java-based IMPGA was verified using a series of real farmland datasets, randomly generated farmland tasks, and agricultural machinery in the Tacheng Prefecture of Xinjiang of western China. The MATLAB software was also used to generate job deployment. The experimental results showed both GA and IMPGA effectively performed the multi-task and multi-machinery assignments. Both GA and IMPGA achieved the optimal solution, when the number of fields was 5. The optimal and average solutions of IMPGA increased by 3.77% and 3.56%, respectively, when the number of fields was 10. The optimal and average solutions of IMPGA increased by 1.63% and 3.76%, respectively, when the number of farmlands was 15. The optimal solution and the average solution of IMPGA were improved by 4.46% and 3.47%, respectively, when the number of farmlands was 20. The total average quality of optimal and average solutions of IMPGA increased by 2.47% and 2.70%, respectively, indicating a better performance of IMPGA deployment. This finding can provide a reasonable scheduling scheme for the cross-regional operation of agricultural machinery in the production of the large-scale unmanned farms.