Clustering is the process of partitioning a set of objects into disjoint groups, each partition is called a cluster. Intuitively, it is desirable that the members in each cluster are very similar to each other in terms of their characteristics. As well, it is desirable to have a low degree of similarity between members in different clusters. In general, clustering algorithms can be categorized to follow either a partitioning, a hierarchical, a density, a model-based or any combination of these approaches. The ADBSCAN algorithm is a density-based clustering algorithm which presents a new method to identify high-density local instances considering the properties of the nearest neighbor graph. Two parameters are used in this algorithm, namely the parameter k representing the number of nearest neighbors, and the percentage of noise in the data set. These parameters have a significant effect on the quality of the output as well as the required time. Therefore, it is necessary to find optimal values for these parameters. Brute-force search is one of the naï, ve ways to this end. However, evolutionary-based algorithms such as genetic search methods can be used to make the search process easy and efficient. In this paper, we applied the genetic algorithm to get optimal values of the parameters. The proposed method led to an 11. 46% improvement in the ARI criterion, on average.