In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. This paper presents a two-level of nondominated solutions approach to MOPSO. The ability of the proposed approach to detect the true Pareto optimal solutions and capture the shape of the Pareto front is evaluated through experiments on well-known non-trivial test problems. The diversity of the nondominated solutions obtained is demonstrated through different measures. The proposed approach has been assessed through a comparative study with the reported results in the literature.