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In this paper, an evolutionary clustering technique is described that uses a new line symmetry based distance measure. Kd-tree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. Adaptive mutation and crossover probabilities are used. The proposed GA with line symmetry distance based (GALSD) clustering technique is able to detect any type of clusters, irrespective of their geometrical shape and overlapping nature, as long as they possess the characteristic of line symmetry. GALSD is compared with existing well-known K-means algorithm. Five artificially generated and two real-life data sets are used to demonstrate its superiority.