The increasing use of e-mail in the world because of its simplicity and low cost, has led many Internet users are interested in developing their work in the context of the Internet. In the meantime, many of the natural or legal persons, to sending e-mails unrelated to mass. Hence, classification and identification of spam emails is very important. In this paper, the combined Particle Swarm Optimization algorithms and Artificial Neural Network for feature selection and Support Vector Machine to classify and separate spam used have and finally, we compared the proposed method with other methods such as data classification Self Organizing Map and K-Means based on criteria Area Under Curve. The results indicate that the Area Under Curve in the proposed method is better than other methods.