Data mining is a critical data analysis technique for extracting hidden information from large databases for business or industrial applications. As the size of organizational databases increase, finding information and knowledge efficiently is essential. In the past, numerous clustering algorithms based on grid-clustering schemes have been proposed. This study proposes, simple-leaping search (SLS), a new grid-based clustering algorithm that partitions the space by the number of grids. It then sequentially searches odd columns of all grids according to the minimal point set at each grid. Based on whether the grid is useful or useless, different neighbor grids are searched. Experimental results show that the SLS clustering algorithm performs better than other clustering algorithms such as DBSCAN, IDBSCAN and GOD-CS.