Clustering, one of the important data mining techniques, has two main processing methods on data-based similarity clustering and space-based density grid clustering. The latter has more advantage than the former on larger and multiple shape and density dataset. However, due to a global partition of existing grid-based methods, they will perform worse when there is a big difference on the density of clusters. In this paper, we propose a novel algorithm that can produces appropriate grid space in different density regions by simulating cell division process. The time complexity of the algorithm is O(n) in which n is number of points in dataset. The proposed algorithm will be applied on popular chameleon datasets and our synthetic datasets with big density difference. The results show our algorithm is effective on any multi-density situation and has scalability on space optimization problems.