In this paper, we consider a scheduling problem for a set of agile Earth observation satellites for scanning different parts of the Earth’s surface. We assume that preemption is allowed to prevent repetitive images and develop four different preemption policies. Scheduling is done for the imaging time window and transmission time domain to the Earth stations as well. The value of each picture from different target regions and the limitations of the satellite constellation in terms of memory and energy cause high computational complexity for this problem and thus obtaining an optimum solution with a deterministic method is very time-consuming. Consequently, a genetic-based metaheuristic algorithm with a specific solution representation is developed in order to maximize the total value of the observation process by establishing heuristic rules in the initial population of this algorithm. Comparison of the results from the proposed model with the results of cases where repetition of observed areas is not ignored indicates that the proposed model can bring about a significant increase in profits in the planning horizon.

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