This study addresses the issues of low parameter setting efficiency and unclear evaluation criteria in suspension systems. A fast parameter optimization and tuning strategy based on the improved particle swarm optimization algorithm (IPSO) is proposed, and an objective function for evaluating suspension performance is constructed. The AMEsim and Simulink software are used to build a 1/4 vehicle semi-active suspension and dual-circuit passive suspension simulation model. Taking a Class C road as input, simulation calculations are performed on indicators such as vehicle body acceleration, suspension dynamic stroke, and tire dynamic load. The results show that the performance of the PID-controlled semi-active suspension system optimized by the IPSO algorithm is superior to that of the optimized dual-circuit passive suspension system, and relevant parameters can be quickly tuned. This study provides theoretical guidance and design basis for the optimization design of vehicle suspension systems, overcomes the subjectivity and inefficiency of traditional parameter settings, and has significant theoretical and practical value.