This paper aims to claim the correlation between PSO and GA to analyze best optimization technique. We have opted Particle Swarm Optimization (PSO) from Swarm based and Genetic Algorithm (GA) from Evolution based, it claims that PSO & GA produces the same effectiveness and moreover PSO is more computationally efficient than GA. Griewangks function is taken as input test function to compare PSO and GA to find out best optimized value. Evolution is the change in gene pool of population from generation to generation by processes such as mutation, selection and crossover. Swarm intelligence is based on nature-inspired behavior and is successfully applied to optimization problems in a variety of fields. In this system interaction between individuals and simple behavior between population and environment usually lead to detection of aggregate behavior, which is typical for whole colony. This could be observe by ants, bees, birds or bacteria in the nature which inspired to develop the algorithm called Swarm-based intelligence and are successfully applied for solving complicated optimization problem. PSO and GA are similar in the sense that these two heuristics are population based search methods. GA has been popular in academia and industry because of its intuitiveness and ability to effectively solve higher non-linear optimization problems. It is based on principles of Genetics and Natural Selection. The main limitation of GA is its effective computational cost. PSO works for flock of birds and is applied to so many areas such as function optimization, artificial neural network training, fuzzy system control and other areas where GA can be applied.