In the WSN – Wireless Sensor Network, the limitations on the sensor’s battery is the prime constraint for achieving optimum network performance. For selecting the CH – Cluster Head, many researchers have proposed several methods for optimization in the WSNs of homogeneous type. But for HWSN – Homogeneous WSN, there is still a huge scope is available for enhancing its capabilities. In this research work, GAOC – Genetic Algorithm based Optimization of Clustering protocol for area optimization is proposed for optimized selection of CH. This protocol uses the parameters like sink distance, density of nodes and residual energy for the fitness function. In addition, to solve the problems with hot-spots and to reduce the distance of communication between nodes and sinks, MS-GAOC Multiple-data Sinks based GAOC was presented. For the fair comparisons, experiments were conducted with MS-GAOC and the designed protocols. By using the results of simulation with respect to the parameters of performance like lifetime of network, period of the stability, dead nodes with respect to rounds, remaining network energy and the throughput of the network, it is inferred that GAOC and the MS-GAOC perform better than the existing protocols. The presented protocols are expected to play prime role in controlling hostile type of applications like detection of fire in the forests, early recognition of volcano eruptions and others