Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve scheduling. In this paper, a combination of genetic algorithms and binary gravitational attraction is used for scheduling problem solving, where the reduction in the duty performance timing and cost-effective use of simultaneous resources are investigated. In this case, the user determines the execution time parameter and cost-effective use of resources. In this algorithm, a new approach that has led to a balanced load of resources is used in the selection of resources. Experimental results reveals that our proposed algorithm in terms of cost-time and selection of the best resource has reached better results than other algorithm.