Many of the emerging scientific applications are in need of enormous computing power, large data bases, visualization tools, data sets etc. The workload in the grid environment is increasing prominently. Scheduling this workload to the resources has become endure. Resources often consist of tens or hundreds or thousands of processors. So the energy consumption of these processors has become a major concern. Since reducing energy incorporates certain benefits of providing a healthier environment with reduction in the operating costs. In this paper, a multi-objective Particle Swarm Optimization (PSO) algorithm is used to enhance the scheduling process in the grid environment.