A multi-objective robust optimization scheme for the powertrain mount system of an electric vehicle is proposed in this paper. A permanent magnet synchronous motor model is established by taking account of the effects of magnetic saturation and space harmonics, in which the d-q-axis inductance and the flux linkage excited by permanent magnet were obtained by finite element method. The rippled output torque of the permanent magnet synchronous motor mixed with harmonic components is obtained with the New European Driving Cycle as the running condition of the electric vehicle. A six degree-of-freedoms (DOFs) powertrain mount system is established and the response of the system is obtained with the rippled torque as the excitation input. A multi-objective optimization model of the powertrain mount system is built with the stiffness's of the mounts as the design variables, and with the goal of maximizing the decoupling rates and minimizing the dynamic reaction forces of the mounts acting on the car body. Genetic algorithm is used to conduct the global optimization and all the Pareto optimal solutions are found out based on the optimization theory, and the solution with the optimal robustness of dynamic reaction force is obtained by Latin hypercube sampling method. The results show that with the proposed multi-objective robust optimization scheme applied for the parameters optimization of the motor mount system, the decoupling rates increase obviously, the dynamic reaction force decreases apparently, and the optimization result shows good robustness. The optimization results can make the powertrain mount system of electric vehicles processing of optimal dynamic response characteristics correspondingly.