A neural network PID method based steering stability controller was proposed by using particle swarm optimization. It aims to improve the steering stability of distributed drive electric vehicle and solve the problems of slow convergence and easy to fall into local optimal solution based on neural network control algorithm. This method can realize the yaw rate control and slip rate control of each wheel by yaw moment and slip rate adjustment torque. Based on this, an optimal torque distribution algorithm under steering condition was studied. The modified torque was obtained by correcting the driving torque with the fuzzy control algorithm. Then the modified torque was optimally distributed together with the yaw moment and slip rate adjustment torque of the stability controller as a quadratic programming problem to obtain the optimal driving torque of each wheel. Finally, the performance comparison tests under typical working conditions such as double lane shifting condition and serpentine condition were carried out based on the joint simulation platform. The results show that this algorithm can maintain the stability of the vehicle while maintaining good vehicle dynamics. The stability controller can reduce the slip phenomenon by 36． 4% under slalom condition, and the optimal torque distribution algorithm can improve the stability by 31． 2% under the double lane change condition.