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Quality of Service is one of the most important research topics in cloud computing, both from the customer's point of view and the cloud service provider's point of view, due to the increasing number of cloud services and applications, along with the significant increase in users and workloads. Task scheduling has been a topic of discussion in many researches, some of which have proposed new ways to improve the Quality of Service in cloud systems. Recently, metaheuristic algorithms have been employed to improve job execution efficiency in such systems, which has proven effective in finding optimal task scheduling solutions. During this research, the latest metaheuristic algorithm Whale Optimization Algorithm (WOA) will be applied for optimizing task scheduling in cloud systems. Additionally, a multi-objective model for optimization will be used to achieve a balance between user satisfaction with these systems, and the requirements needed by service providers. Research results indicate WOA superiority in minimizing the execution time cost, price cost, and total cost parameters as contrasted with the current metaheuristic algorithms, which ultimately result in higher service quality. [ABSTRACT FROM AUTHOR]