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In grid environment the numbers of resources and tasks to be scheduled are usually variable. This kind of characteristics of grid makes the scheduling approach a complex optimization problem. Genetic algorithm (GA) has been widely used to solve these difficult NP-complete problems. However the conventional GA is too slow to be used in a realistic scheduling due to its time-consuming iteration. This paper presents an improved genetic algorithm for scheduling independent tasks in grid environment, which can increase search efficiency with limited number of iteration by improving the evolutionary process while meeting a feasible result.