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A grid scheduling algorithm inspired on the simulated annealing meta-heuristic (SA) is presented. Guided by predictions about parallel applications resource usage, the algorithm uses SA to find a near-optimal solution which minimizes the overall execution time for scheduling problems on heterogeneous grids. The scheduling algorithm invalidated by simulations, using a model which considers the mainly details about the distributed computers and the jobs,and is compared with other scheduling algorithms. The results allows identify the best SA parameters values, that are the learning rate and the iteration size, for each grid size. We found good results when simulating the scheduling algorithm for grid sizes near by 512 computers. We also observed that the SA computing cost do not jeopardize the scheduling results, which are better when compared with the performance of other scheduling disciplines.