An intrusion detection system is the process for identifying attacks on network. Choosing effective and key features for intrusion detection is a very important topic in information security. The purpose of this study is to identify important features in building an intrusion detection system such that they are computationally effcient and effective. To improve the performance of intrusion detection system, this paper proposes an intrusion detection system that its features are optimally selected using genetic algorithm optimization. The proposed method is easily implemented and has a low computational complexity due to use of a simplified feature set for the classification. The extensive experimental results on the NSL-KDD intrusion detection benchmark data set demonstrate that the proposed method outperforms previous approaches, providing higher accuracy in detecting intrusion attempts and lower false alarm with reduced number of features.