Particle swarm optimization (PSO) has proved its ability in solving complex engineering design optimization problems. In this paper, we propose some new strategies to improve the search performance of multi-objective particle swarm optimization (MOPSO). The new version MOPSO adopts nonlinear decay approach to adjust the inertia weight, uses mutation strategy, and introduces "Function Stretch" technology. Meanwhile, modified quick sort method is applied to reduce the computation time, crowding distance is used for replacement and maintenance of external repository, new selection scheme for global best solution is proposed, and two groups parallel computation strategy is employed. Two typical multi-objective optimization problems are used to test the proposed algorithm, and experimental results show that the improved MOPSO is effective to obtain well-distributed Pareto optimal solutions and has better performance than multi-objective genetic algorithm.