In this study, a satellite grouping method based on the entropy weighting method TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) scoring combined with a greedy algorithm is proposed to optimise the efficiency of resource allocation in large-scale satellite mission planning, to improve mission completion and to reduce planning time. In this method, we firstly use the scoring and greedy algorithm to optimise the resource allocation efficiency in large-scale satellite mission planning. In this method, firstly, weights are assigned to multiple indicators of coverage frequency, cloud value, data quality, and observation interval of the grid by entropy weighting method to eliminate subjective bias. Subsequently, the satellite observation area is prioritised using the TOPSIS scoring method to ensure that the reasonableness and importance of each task is scientifically assessed. Finally, the satellite resources are grouped and allocated according to the priority level through the greedy algorithm to maximise the resource utilisation efficiency and task completion. The experimental results show that the method exhibits significant advantages under multi-task and multi-constraint conditions, and can effectively improve the task completion rate and resource utilisation.