Abstract: The problem of unsupervised and supervised learning of RBF networks is discussed with Multi-Objective Particle Swarm Optimization (MOPSO). This study presents an evolutionary multi-objective selection method of RBF networks structure. The candidates of RBF networks structures are encoded into particles in PSO. These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity. This study suggests an approach of RBF network training through simultaneous optimization of architectures and connections with PSO-based multi-objective algorithm. Present goal is to determine whether MOPSO can train RBF networks and the performance is validated on accuracy and complexity. The experiments are conducted on two benchmark datasets obtained from the machine learning repository. The results show that; the best results are obtained for our proposed method that has obtained 100 and 80.21% classification accuracy from the experiments made on the data taken from breast cancer and diabetes diseases database, respectively. The results also show that our approach provides an effective means to solve multi-objective RBF networks and outperforms multi-objective genetic algorithm.