The gap between processor speed and memory access performance increases continuingly. In order to improve performance degradation due to memory accesses, we can reduce memory block accesses by improving data locality. In this research, we focus on optimizing data locality in loops. We adopt an iterative greedy algorithm. It iteratively perform loop fission and loop fusion by moving strongly-connected components among loops subject to data dependence constraints. It evaluates data locality change of each move and decide if accept such move. Subsequently loops can be improved gradually forward optimal result.