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In this paper, we present a new approach that helps managers to optimize task allocation and work load distribution using multiobjective particle swarm optimization (MOPSO). A new algorithm has been introduced to increase number of nondominated solutions (Pareto front size), by using inheritance of nondominated solutions density estimators and modifying density estimation algorithm. The performance of the new algorithm is evaluated on test functions and metrics from literature. The results show that the proposed algorithm is competitive in converging towards the Pareto front and generates a well distributed set of nondominated solutions. The new approach helps managers to avoid juggling many objectives to develop a project plan. These include minimizing cost, defects, and completion time; and maximizing worker utilization and customer satisfaction. Many of these objectives are conflicting. For example, a demand to decrease completion time clashes with a goal to minimize defects.