The invention relates to a satellite scheduling method (1) including: a) producing initial scheduling plans (105) on the basis of input requests related to tasks to be performed within a given time period by one or more remote sensing satellites; wherein in each of said initial scheduling plans respective tasks are scheduled, which do not conflict with each other in time and in using satellite resources of the remote sensing satellite(s); and wherein each of the tasks to be performed is scheduled in at least one of the initial scheduling plans; b) applying a genetic-algorithm-based processing (108) to the initial scheduling plans to produce a genetic-algorithm-based scheduling plan which is optimized with respect to given mission objectives, and complies with given constraints related to the satellite resources, to the tasks to be performed, and to the given time period; and c) applying a simulated-annealing-based processing (109) to the genetic-algorithm-based scheduling plan to produce a simulated-annealing-based scheduling plan that fits the given mission objectives, that complies with the given constraints, and in which a larger number of tasks is scheduled than in the genetic-algorithm-based scheduling plan. In particular, the step b) includes carrying out a genetic-algorithm-based iterative procedure comprising: at a first genetic-algorithm-based iteration, selecting a subset of the initial scheduling plans on the basis of the given mission objectives (202,203), and applying crossover (205), mutation (206) and elitism (207) techniques based on respective predefined genetic evolution factors to the selected sub-set of the initial scheduling plans to produce evolved scheduling plans complying with the given constraints; at each genetic-algorithm-based iteration following the first one, selecting, on the basis of the given mission objectives, a subset of the evolved scheduling plans produced at the preceding genetic-algorithm-based iteration (202,203), and applying the crossover (205), mutation (206) and elitism (207) techniques to the selected sub-set of the evolved scheduling plans produced at the preceding genetic-algorithm-based iteration to produce new evolved scheduling plans complying with the given constraints. Moreover, said step b) further includes: stopping carrying out the genetic-algorithm-based iterative procedure when given genetic-algorithm-related stopping criteria are met (208,209); and automatically selecting, among the evolved scheduling plans produced at the last genetic-algorithm-based iteration performed, the one which best fits the given mission objectives. Additionally, the satellite scheduling method (1) includes also: computing an intersection matrix (103) representing conflicts in time and in using the satellite resources of the tasks to be performed within the given time period; computing a plan complexity (103) on the basis of the intersection matrix; and computing the given genetic-algorithm-related stopping criteria on the basis of the intersection matrix (104,104a); wherein the initial scheduling plans are produced on the basis of said intersection matrix (105).
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