This paper mainly investigates finding out Nash equilibria of finite strategic games by a computational intelligence technique, particle swarm optimization (PSO). We focus on the computation of all Nash equilibria for complete-information, static, noncooperative game, including single-object optimization and multi-object optimization (MOO). Here we simplify the algorithm of detecting multiple equilibria for single-object games, as well as ameliorate the Multi-Object Particle Swarm Optimization (MOPSO) algorithm proposed by Julio E. Alvarez, etc, finding that our algorithm is more efficient and with less error ratio when searching for the Pareto front of multi-object optimization, comparing with other computational intelligence methods.