The problem of identifying core patterns with the correct neighborhood parameters is a major challenge for density-based clustering techniques derived from the popular DBSCAN algorithm. An evolutionary approach to optimizing the assignment of core patterns is proposed in this paper. Key ideas presented here include a genetic representation that associates distinct neighborhood parameters with potential core patterns and specialized crossover and mutation operators. The evolutionary framework is based on the multi-objective NSGA-II algorithm, with simplified fitness measures derived from local (neighborhood) information. Clustering experiments on both synthetic and benchmark clustering datasets are presented and results are compared to the original DBSCAN, fuzzy DBSCAN and k-means.