Abstract. In the context of data mining, classification rule discovering is the task of designing accurate rule based systems that model the useful knowledge that differentiate some data classes from others, and is present in large data sets. Iterated greedy search is a powerful metaheuristic, successfully applied to different optimisation problems, which to our knowledge, has not previously been used for classification rule mining. In this work, we analyse the convenience of using iterated greedy algorithms for the design of rule classification systems. We present and study different alternatives and compare the results with state-of-the-art methodologies from the literature. The results show that iterated greedy search may generate accurate rule classification systems with acceptable interpretability levels.