Abstract: The Grid has emerged as an attractive platform to tackle various large scale problems, especially in science and engineering. The objective of this study is to generate an optimal schedule and evaluate heuristic algorithm namely Simulated Annealing (SA) with other scheduling algorithms for data intensive grid applications that maximizes Job completion ratio and minimizes lateness in job completion. The 2 objectives are aimed at achieving simultaneously by the scheduling mechanism. In addition, to create neighborhoods for simulated annealing, 3 Perturbation schemes, viz. pair wise exchange, insertion and random insertion perturbation are used. The results pinpoints that the proposed Simulated Annealing search mechanism performs well compared to other scheduling mechanisms on considering the workflow execution time within the deadline and lateness of individual workflows.