The particle swarm optimization (PSO) is a new swarm intelligence technique inspired by social behavior of bird flocking. In this paper, the optimal multi-objective optimization based on PSO (OMOPSO) is presented. Since the parameters determines the performance of the algorithm, the uniform design is introduced to obtain the optimal combination of the parameters. Additionally, a new crowding operator is used to improve the distribution of nondominated solutions, and ?-dominance is used to fix the size of the set of final solutions. OMOPSO is also applied to optimize the indices of blind color image fusion. First the model of blind color image fusion in YUV color space is established, and then the proper evaluation indices without the reference image are given, in which a new indices of conditional mutual information is proposed. Experimental results indicate that OMOPSO has better exploratory capabilities, and that the approach to blind color image fusion realizes the Pareto optimal blind color image fusion.