Energy consumption has emerged as a primary issue in the deployment of Wireless Sensor Networks (WSNs). This issue arises due to the energy constraints associated with the Sensor Nodes (SNs) which are considered as their basic component. Since, the functioning of the WSNs completely relies on the sensing and communicating ability of the sensor nodes so the fore most challenge is how to minimize the power dissipation of these nodes. Clustering proved to be an appropriate mechanism to deal with this issue [1]. This paper investigates three partitional clustering algorithms namely K-means, K-medoids, Fuzzy C Means by incorporating an evolutionary technique Differential Evolution (DE). Performance comparison of the proposed algorithms has been done on the basis of the metrics like network throughput, network lifetime and dead nodes per round. The simulation results have shown that suggested algorithm, hybrid K-means/DE performs better than its counterparts.