Abstract: Cloud computing environment can offer dynamic and elastic virtual resources to cloud users on demand. It becomes an attractive challenge that how task scheduling satisfy the dynamic requirements of users and utilize the virtual resources efficiently in cloud environment. Particle Swarm Optimization (PSO) is a global metaheuristic method to solve optimization issues. The processing capacity (cost) and makespan associated with the task schedule and the resources allocated are taken into account to measure the performance of optimization algorithm in this study. PSO based fitness function scheduling heuristic to balance the load across the entire system is introduced while trying to minimize the makespan and increase the processing capacity. For comparison, ant colony algorithm is presented to simulate on the same datasets. The experiments results show that PSO based fitness function is more effective and efficient with shorter completion time and lower cost.