In this paper, a multi-objective particle swarm optimization algorithm with a new global best (gbest) selection strategy is proposed for dealing with multi-objective problems. In multi-objective particle swarm optimization, gbest plays an important role in convergence and diversity of solutions. A K-means algorithm and proportional distribution based approach is used to select gbest from the archive for each particle of the population. A symmetric mutation operator is incorporated to enhance the exploratory capabilities. The proposed approach is validated using seven popular benchmark functions. The simulation results indicate that the proposed algorithm is highly competitive in terms of convergence and diversity in comparison with several state-of-the-art algorithms.