Grid computing is a high performance computing environment to solve larger scale computational demands. This technology, which is now considered as an alternative to supercomputers, contains different issues such as resource management, job scheduling, security problems, and information management. However, job scheduling is a fundamental issue in achieving high performance in grid computing systems. Most of the applications are a set of interdependent tasks. Complete execution of all tasks with a special task’s sequence will tend to the goal of the application. This type of the applications is known as workflow. By developing workflow management systems the grids become able to design, manage and execute different type of the workflows. One of the important issues in this system is to find workflow scheduling solutions and assign tasks to grid resources. Scheduling solution has an important effect on system performance and should have optimal execution time and cost. In this thesis, a new workflow scheduling method in grid environment is proposed that it is able to generate a set of optimal scheduling solutions with low consumed time. The proposed method is based on multi-objective genetic algorithm to optimize workflow execution time and cost simultaneously. In this model workflows are divided to some sequential levels that can help to remove checking workflow dependencies during the scheduling process. New definitions and operators for this model are proposed and implemented. Then, the proposed method in different aspects is widely compared with similar methods. The simulation results clearly show that the proposed method has a good performance in comparison to the other well-known methods. Keywords: grid computing, scheduling, multi-objective genetic algorithm, workflow.