[Objective] To further improve the performance of automotive permanent magnet synchronous motors (PMSM) and address the inefficiency of traditional optimization methods for motors, a multi-objective optimization design method for automotive permanent magnet synchronous motors is proposed based on improved generalized regression neural network (GRNN) and improved salp swarm optimization algorithm. [Methods] Firstly, a parameterized motor model was constructed, and the motor was scanned through the finite element model to obtain sample data of the motor's structural parameters and corresponding performance. Then the model was built using GRNN. With the objectives of minimizing the peak-to-peak value of cogging torque, maximizing the rated average torque, and minimizing torque ripple, the structural parameters of the motor were optimized using the salp swarm algorithm. [Results] The optimized motor’s average torque increased by 2%, the torque ripple decreased by 16%, and the cogging torque decreased by 60.58%. This demonstrates the efficiency and accuracy of the method. [Conclusion] The proposed method can fully utilize computing power resources for parallel computing. Compared to traditional optimization methods, it can achieve multi-objective optimization design of motors more quickly and effectively.