Genetic algorithm: Genetic algorithm is considered as evolutionary algorithm. The normal genetic algorithm is based on simulation of genetic mechanism and theory of biological evaluation[10]. A GA is utilized to look for close ideal arrangements when no deterministic strategy exists or if the deterministic technique is computationally difficult. A lot of work has been done from last few years in optimized deployment of wireless sensor network. Shubhpreet and Uppal et al.[1] have implemented the Genetic algorithm to remove overlapping of sensor nodes and to ensure maximum coverage. Their proposed algorithm was successful to achieve maximum coverage and intersection free deployment but still there exists an issue of energy consumption. Yoon and Kim et al.[7] additionally characterized Maximum Coverage Sensor Deployment Problem (MCSDP) in their paper and attempted to evacuate issue utilizing proposed efficient genetic algorithm using normalization function, likewise embraced Monte Carle technique to outline efficient evaluation function. To overcome the problem of overlapping and coverage Brar and Virk et al.[3] have proposed a new method using GA, the algorithm then ensures the maximum coverage with interference free nodes. Advantages of Genetic algorithm: i. Medium computation. ii. Optimal solutions are provided by GA in deployment. Disadvantages of genetic algorithm: i. High memory requirements. ii. Lack of flexibility. Particle swarm optimization: is a branch of Artificial knowledge that spotlights on the aggregate conduct and properties of perplexing, self-composed, decentralized framework with a social structure for example flying creature runs, insect states and fish schools [5]. Particle Swarm Optimization is an optimization approach which have been previously implemented in wireless sensor network. Kulkarni et al.[8] have been studied this technique for maximum deployment, localization, clustering of nodes and data aggregation. PSO provides high quality results, fast merging and inconsequential calculation. Then again PSO requires vast measure of memory which restrict its utilization in rapid constant applications. Soliman and tan et al.[11] have been applied adaptive hybrid optimization, PSO and GA to eliminate sensor location problem for maximum coverage. The method ensure true results for desired coverage. Features of particle swarm optimization: i. Easy implementation on hardware and software. ii. Availability of guidelines for choosing its parameters. iii. High quality solutions.>