In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such asautomatic k-determination and a set of cluster validity indices concurrently. The proposed automaticclustering technique uses the most recent optimization algorithm Jaya as an underlying optimizationstratagem. This evolutionary technique always aims to attain global best solution rather than a local bestsolution in larger datasets. The explorations and exploitations imposed on the proposed work results todetect the number of automatic clusters, appropriate partitioning present in data sets and mere optimalvalues towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performanceof aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clusteringoptimized with evolutionary approaches decipher into realistic and scalable performance paybacks.