Abstract: This study presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for optimal clustering N object into K clusters. The new ACO-SA algorithm is tested on several data sets and its performance is compared with those of ACO, SA and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for data clustering.