In multi-objective particle swarm optimization (MOPSO), the selection of global guides for all partials is vital to improve the convergence and diversity of solutions. In this paper, the related work of global guides searching in MOPSO is introduced, and a new Pareto–based selecting strategy is proposed. Basing on the analysis of the structure and mapping relation of the particle swarm and the nondominated solutions archive, considering the density information, the global guides selecting frequency and other factors, a new gbest selecting strategy for each particle in the swam is presented. Experimental results of contrasting experiments of two typical MOPSO functions demonstrate that the proposed strategy is satisfying.