Spam is defined as redundant and unwanted electronica letters, and nowadays, it has created many problems in business life such as occupying networks bandwidth and the space of user’s mailbox. Due to these problems, much research has been carried out in this regard by using classification technique. The resent research show that feature selection can have positive effect on the efficiency of machine learning algorithm. Most algorithms try to present a data model depending on certain detection of small set of features. Unrelated features in the process of making model result in weak estimation and more computations. In this research it has been tried to evaluate spam detection in legal electronica letters, and their effect on several Machin learning algorithms through presenting a feature selection method based on genetic algorithm. Bayesian network and KNN classifiers have been taken into account in classification phase and spam base dataset is used.