Electric vehicles equipped with in-wheel motors are particularly vulnerable to vertical vibration because the increased unsprung mass and motor-induced excitation can simultaneously degrade ride comfort and road friendliness. This study proposes a weighted multi-objective optimization framework for electric vehicle suspension design using a Genetic Algorithm (GA). A quarter-car dynamic model was developed to capture the coupled effects of stochastic road excitation and vertical motor excitation under realistic operating conditions. The suspension stiffness and damping coefficients were selected as the design variables, while the root mean square vertical body acceleration, awbz, and the dynamic load coefficient (DLC) were incorporated into a unified objective function to achieve a balanced trade-off between ride comfort and pavement-friendly performance. The governing equations were implemented in Matlab/Simulink and integrated with the GA-based optimization procedure, with practical constraints imposed on suspension natural frequency and damping ratio. Under the condition of a vehicle traveling on an ISO Class B road surface at 80 km/h with motor excitation at 50 Hz, the optimized suspension parameters reduced awbz by 13.3% and DLC by 11.2% compared with the original configuration. These results demonstrate that the proposed approach can effectively improve ride comfort while simultaneously mitigating the dynamic wheel load transmitted to the road surface, thereby providing a practical basis for suspension parameter selection in in-wheel-motor electric vehicles.