Intrusion Detection Systems (IDSs) deal with large amount of data containing irrelevant and redundant features, which leads to slow training and testing processes, heavy computational resources and low detection accuracy. Therefore, the features selection is an important issue in intrusion detection. In this paper, we investigate the use of evolution algorithms for features selection approach in IDS. We compared the performance of three feature selection algorithms: Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Differential Evolution (DE) using KDD Cup 1999 dataset. Our results show that DE is clearly and consistently superior compared to GAs and PSO for feature selection problems, both in respect to classification accuracy as well as number of features.