###### Abstract  
This study focuses on the integrated scheduling issues of imaging and data transmission for Earth observation satellites, where each target needs to be imaged and transmitted within a feasible time window. The scheduling process also takes into account the constraints of satellite energy and storage capacity. In this paper, a mixed-integer linear programming (MILP) model for the integrated scheduling of imaging data transmission has been proposed. The MILP model was validated through numerical experiments based on simulation data from SuperView-1 series satellites. Additionally, some neighborhood mechanisms are designed based on the characteristics of the problem. Based on the neighborhood mechanisms, the rule-based large neighborhood search algorithm (RLNS) was designed, which constructs initial solutions through various scheduling rules and iteratively optimizes the solutions using multiple destroying and repairing operators. To address the shortcomings of the overly regular mechanism of the destruction and repair operator for large neighborhood search, we design a genetic algorithms (GA) for tuning the heuristic scheduling rules. The calculation results demonstrate the effectiveness of RLNS and GA, highlighting their advantages over CPLEX in solving large-scale problems. satellite mission planning; integrated scheduling of imaging and data transmission; rule-based large neighborhood search algorithm (RLNS); genetic algorithms (GA); mixed-integer linear programming (MILP)  
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task planning of Earth observation satellites, designing a reasonable scheduling scheme is crucial for making full use of satellite resources. There has been a lot of research on the scheduling problems of Earth observation satellites. [PERSON] et al. (2018) transformed the satellite observation problem into a Selective Traveling Salesman Problem (STSP), converted the time window constraints of the satellite into the time window constraints of the vehicle task, and used the Unified Tabu Search Algorithm (UTSA) to solve the Earth-observation satellite task scheduling problem. [PERSON] (2018) conducted an in-depth study of the Satellite Range Scheduling Problem (SRSP) under the Air Force Satellite Control Network (AFSCN) and proposed a genetic algorithm based scheduling algorithm called \"Genitor\" to solve the AFSCN problem, which achieved a high degree of optimization. [PERSON] et al. (2018) investigated the agile Earth observation satellite scheduling problem by using dual decision variables to represent the mission time window of an Earth observation satellite and the start moment of the mission within the time window. [PERSON] et al. (2018) represented the satellite mission planning problem as a weighted directed acyclic graph, and the solution goal of the problem was to find a longest path of the graph. The advantage of this method is that the model is intuitive, easy to understand, and can be solved with the help of mature graph theoretic solving algorithms, but its shortcomings are that the expressive ability of the graph theoretic model is limited, and it is not possible to realize complex mission constraints such as area target imaging, imaging digital transmission, solid-state erasure, and so on, in the model, which is still to be improved for real problem portrayal. Integrated scheduling, solid-storage erasure, and other complex task constraints in the model, and the portrayal of practical problems, is still to be improved. [PERSON] et al. (2018) conducted a constrained modeling of the imaging data transmission integration scheduling problem, but only provided a formal description of the issue based on analysis, without presenting algorithms or computational results. [PERSON] et al. (2018, 2019) studied the scheduling problem of the COSMO-SkyMed radar constellation, analyzing in detail the occupancy and release of onboard storage.