In order to enhance the performance and convergence of multi-objective particle swarm optimization (MOPSO) algorithm for multi-objective optimization, a multi-neighborhood cycle chain structure of multi-objective particle swarm optimization (MNCS-MOPSO) was proposed. Firstly, the population was divided into many neighborhoods. The mutual overlaps were existed between the adjacent neighborhood, and updating strategy was used for different velocity and position aimed at particles of different positions. In addition, velocity control strategy was adopted for all particles and differential evolution strategy was introduced to make disturbance. Comparing with NSGA-II, SPEA2, MOEA/D-DE, SMPSO and OMOPSO by testing 14 unconstraint and 3 constrain benchmark functions, simulation experiments showed that the proposed algorithm could obtain a more uniform distribution of Pareto solution set, and better convergence as well as diversity than those state-of-the-art multi-objective metaheuristics. In order to verify the performance of MNCS-MOPSO algorithm, classical 72 bar truss sizing optimization problems were used to demonstrate the feasibility and effectiveness of this algorithm, and the results were compared with other optimization methods. The results indicate that the MNCS-MOPSO provides better performance in the diversity, the uniformity and the convergence of the obtained solution than other methods.