Grid computing environments have emerged following the demand of scientists to have a very high computing power and storage capacity. One among the challenges imposed in the use of these environments is the performance problem. To improve performance, scheduling technique is used. Most existing scheduling strategies in Grids only focus on one kind of Grid jobs which can be data-intensive or computation-intensive. However, only considering one kind of jobs in scheduling does not result in suitable scheduling in the viewpoint of all system, and sometimes causes wasting of resources on the other side. To address the challenge of simultaneously considering both kinds of jobs, a new Hybrid Job Scheduling (HJS) strategy is proposed in this paper. At one hand, HJS algorithm considers both data and computational resource availability of the network, and on the other hand, considering the corresponding requirements of each job, it determines a value called W to the job. Using the W value, the importance of two aspects (being data or computation intensive) for each job is determined, and then the job is assigned to the available resources. The simulation results with OptorSim show that HJS outperforms comparing to the existing algorithms mentioned in literature as number of jobs increases.