Identical Parallel Machine Scheduling (IPMS) problem for minimizing make span and number of tardy jobs simultaneously is considered as very important production scheduling problem but there have been many difficulties in solving large scale IPMS problem with too many jobs and machines. In order to minimize make span and number of tardy jobs simultaneously improved versions Particle Swarm Optimization (PSO) is proposed to enhance scheduling efficiency with less computational burden. The premature convergence at the initial stages of iteration is considered as the major drawback for standard PSO. However, this can be avoided by incorporating mutation a common genetic algorithm operator into the standard PSO and is termed as MPSO. Several numerical examples demonstrate the MPSO proposed is efficient and fit for large scale IPMS problem for minimizing the objectives considered. The solution obtained by MPSO outperforms standard PSO. © 2006-2016 Asian Research Publishing Network (ARPN).