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Task Scheduling is one of the most challenging problems in cloud computing. It is an NP-Hard and plays an important role in optimizing the use of available resources. Recently, Multi-Objectives Genetic Algorithm (MOGA) is proposed for cloud tasks scheduling. However, the execution time of the GA is higher than Particle Swarm Optimization (PSO), and the convergence is slower. PSO converges fast because it can be implemented without too many parameters and operators. In this paper, Multi-Objectives PSO (MOPSO) and MOPSO with Importance Strategy (IS)(MOPSO_IS) algorithms are proposed. MOPSO algorithm is integrated with the IS to select the global best leader. Furthermore, incorporating a mutation operator in MOPSO_IS resolved the problem of premature convergence to the local Pareto-optimal front. The performance of the proposed algorithms was compared with MOGA and produced better …