Abstract: In recent years, there has been a significant and rapid expansion in the satellite field, with a corresponding increase in the demand for Earth observation. This led to a growing need for sophisticated management of Electromagnetic Spectrum Monitoring Satellite (ESMS) missions. Neglecting to incorporate dynamic adjustments in satellite mission planning will lead to a considerable loss of time and resources. Dynamic adjustments to missions and allocation of appropriate satellite resources are crucial for the effective execution of monitoring tasks. This paper begins by developing a mission planning model that incorporates dynamic adjustments. Subsequently, we introduce a Two-layer Hybrid Scheduling Approach (TH-SA) designed for task flexibilty. The approach uses a genetic algorithm in the first layer to deal with non-dynamically adjustable task sequences. The second layer relies on heuristic rules to plan dynamically adjustable tasks. A rule-based initialization strategy and diverse crossover patterns enhance the exploration and exploitation efficiency of the genetic algorithm, while the heuristic algorithm optimizes the scheduling of dynamically adjustable tasks through task reconfiguration and resource allocation. By categorizing and processing tasks, the algorithm enhances the efficiency of planning for dynamically adjustable tasks and ensures the completion rate of those that are not dynamically adjustable. Finally, simulation experiments confirm that the algorithm maintains high performance in task planning of varying scales, demonstrating its effectiveness in improving the performance of Electromagnetic Spectrum Monitoring Satellite task planning.