While planning and designing the operation of the wireless sensor network, major focus is always on balancing the energy consumption, lifetime of the node and data aggregation. This may be achieved through suitable clustering technique which helps in minimizing the distance between cluster head nodes and associated cluster members. Some of the algorithms like, LEACH, GCA (Genetic Clustering Algorithm), ERP (Evolutionary Routing Protocol), EAERP (Energy Aware Evolutionary Routing Protocol), HSA (Harmony Search Algorithm) work on similar lines. Due to variety of constraints inherently present in WSN, the objective function of clustering technique is of multi-objective nature. One of the objectives may be of minimization type whereas other may be of maximization type. Due to conflicting goals of objective functions it might be difficult to find a unique and optimal solution. This paper examines multiple solutions and compares performance of these candidate solutions for their applicability in the clustering process of WSN. Pareto optimality concept has been incorporated in the proposed work for multi-objective problem of clustering in WSN. Multiple objectives based approach with customized stopping criteria in the proposed work results in to near optimal solutions with reduced simulation time requirements and improved network performance in terms of energy usage, data delivery from the sensor nodes to the base station.