Breast cancer (BC) disease is considered as a leading cause of death among women in the whole world. However, the early detection and accurate diagnosis of BC can ensure a long survival of the patients which brought new hope to them. Nowadays, data mining occupies a great place of research in the medical field. The Classification is an effective data mining task which are widely used in medical field to classify the medical dataset for diagnosis. Based on the BC dataset, if the training dataset contains non-effective features, classification analysis may produce less accurate results. To achieve better classification performance and increase the accuracy, feature selection (FS) algorithms are used to select only the effective features from the overall features. This paper proposed a suboptimum FS algorithm based on the wrapper approach as evaluator and Particle Swarm Optimization (PSO) as a search method for the classification of BC dataset. The proposed PSO-FS algorithm uses a PSO algorithm to estimate and search for the significant and effective features subset from overall features set. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Bayes Network (Bayes net) classifiers were used as evaluators to the optimized feature subset out from PSO search method. The Experimental results showed that the proposed PSO-FS algorithm is more effective by comparing with other two traditional FS search methods which are Beast First, and Greedy Stepwise in terms of classification accuracy and performance