Grid computing came into being an active research area because of the advances in wide-area network technologies and the low cost of computing resources. One motivation of grid computing is to aggregate the power of distributed resources and integrate the resources into a unified platform. To minimize the total completion time of the submitted computing jobs to a grid platform, people employ various scheduling algorithms to dispatch the jobs to the resources. However, it has been proved that the optimal scheduling algorithm is NP-hard. Therefore, many people turn to use heuristic approaches for grid scheduling. In this paper, we introduce ten common scheduling heuristics to schedule a combination of job-chains (linear-dependent jobs) and independent jobs on a heterogeneous environment. We implemented these methods on a grid simulator to evaluate their performance under different circumstances. The results of scheduling job-chains and independent jobs on a heterogeneous environment are quite different from previous studies, and we provide our explanations for the differences. We also propose a hybrid method based on our observation, and the simulation results show that it has good performance in terns of makespan.