This article considers the problem of scheduling n jobs on m identical parallel processors where an optimal schedule is defined as one that produces minimum makespan (the completion time of the last job) and total tardiness among the set of schedules. Such a problem is known as identical parallel processor makespan and total tardiness problem. In order to minimize makespan and total tardiness of identical parallel processors, improved versions of particle swarm optimization and harmony search algorithm are proposed to enhance scheduling performance with less computational burden. The major drawback of particle swarm optimization in terms of premature convergence at initial stage of iterations is avoided through the use of mutation, a commonly used operator in genetic algorithm, by introducing diversity in the solution. The proposed algorithm is termed as particle swarm optimization with mutation. The convergence rate of harmony search algorithm is improved by fine tuning of parameters such as pitch adjusting rate and bandwidth for improving the solution. The performance of the schedules is evaluated in terms of makespan and total tardiness. The results are analyzed in terms of percentage deviation of the solution from the lower bound on makespan. The results indicate that particle swarm optimization with mutation produces better solutions when compared with genetic algorithm and particle swarm optimization in terms of average percentage deviation. However, harmony search algorithm outperforms genetic algorithm, particle swarm optimization, and particle swarm optimization with mutation in terms of average percentage deviation. In certain instances, the solution obtained by harmony search algorithm outperforms existing clonal selection particle swarm optimization.