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The distributed drive system allows for independent control of each wheel, providing greater maneuverability and adaptability to various terrains and working conditions. Additionally, when combined with electric technology, the distributed drive system can reduce emissions, decrease reliance on fossil fuels, and improve sustainability. These advantages position distributed drive electric tractor (DDET) as having broad potential for applications in agriculture and industry. Aiming at the low traction efficiency and high energy consumption of the DDET, a distributed drive system parameter optimization design and verification method based on the multi-island genetic algorithm (MIGA) was proposed. According to the working conditions of plowing operations, a 7-DOF coupled dynamics model of the tractor distributed drive system and a tire-soil interaction model were established. The parameter design and matching selection of key components in the drive system were completed. An MIGA-based optimization strategy for the front and rear wheel side transmission ratios (WTR) was proposed, taking WTR as the decision variable, minimizing energy losses in the drive system as the optimization objective, and with constraints on the power and speed of the drive motor. This effectively prevented the algorithm from prematurely falling into local optima during the optimization process, improving the efficiency and reliability in obtaining the globally optimal. A Matlab/Simulink-NI PXI joint simulation platform was built to verify the correctness and real-time executability of the parameter optimization strategy. The joint simulation results showed that the distributed drive system optimized based on MIGA achieved effective performance improvements. Under cyclic plowing conditions, the average traction of the tractor was 10.610N with maximum traction power of 31.25kW. The average efficiency was increased by 0.38% and energy consumption of the drive motor was decreased by 7.53%. The research result can provide theoretical foundations and verification methodologies for the optimal design and system control of distributed drive electric tractors.