Cloud computation is a model for easy on-demand access of the user to host of configurable and changeable computation resources like networks, storage, servers, services, and applications with nominal management of resources or service provider interaction. Task scheduling is regarded as a fundamental issue in cloud computing which aims at distributing the load on the different grids of a distributed system for optimizing response time and resource utilization. In this research, a method for

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... imization of task scheduling to enhance the cloud's efficiency is presented. Within the proposed method, three criteria for scheduling in the cloud have been considered which include the task implementation time, the task transfer time and the task implementation cost. In this method, not only the total task time is reduced, but also the maximum time of task implementation will be minimized. In this method, the Multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) was utilized. After evaluating the proposed method the total task implementation of 112.46 for, the total task transfer time of 2410 and the total cost of 242.4 were obtained. The findings represent the superiority of the suggested method over Multi-Objective Particle Swarm Optimization.