A multi-objective particle swarm optimization algorithm based on dynamic crowding distance (DCD-MOPSO) was proposed. Applying the improved quick sorting to reduce the time for computation, both the dynamic inertia weight and acceleration coefficients were used in the algorithm to explore the search space more efficiently. A new diversity strategy called dynamic crowding distance was used to ensure sufficient diversity amongst the solutions of the non-dominated fronts. Some benchmark functions and the optimization of four-bar plane truss were tested to compare with the performance of DCD-MOPSO and NSGA-Ⅱ. The results show that DCD-MOPSO has better convergence with even distributing of Pareto set.