## 1 Introduction  
With the development of aerospace technology, there are kinds of spacecraft provide excellent opportunities to gather data from space. They are used for obtaining information specifically requested by users or storing data that has been obtained during transmission. No matter what the mission is, it is essential to obtain the data down to the ground station through the downlink. For instance, the earth observation satellite (EOS) is a satellite equipped with various sensors. The main task of EOS is to detect the target area proposed by the user, and then download the detected data to the ground station through the downlink and then deliver it to the user. However, the capacity of the ground station (GS) is very limited compared with the large number of spacecraft and the data waiting to be transmitted. At the same time, some special tasks (e.g., fire spot detection in large-scale wildfires, etc.) need to deliver observation results to users with the shortest possible delay. Taking low-latency data transmission and resource utilization into account, there is a growing need for efficient satellite and ground station data downlink schedule methods. According to the satellite database of the Union of Concerned Scientist [1], as of 1 January 2022, there are 4852 satellites orbiting the Earth over the world. Although the number of satellites continues to increase, it is still unable to meet the increasing user demand. One possible way is to develop low-cost small satellite, which present some technical limitations [2]. The main technical limitation considered in this paper is the inability to maintain the inter-satellite link (ISL) due to its low attitude control accuracy. This prevents it from using ISL and relay satellites to send data down to a ground station, which means that they can only deliver the results of mission to users by communicating with the ground station. More importantly, some special requests have to be satisfied as soon as possible. For example, EOS is always used to observe and photograph specific areas in the task of natural disaster rescue. Observations are required to reach decision makers as quickly as possible. This first challenge is the multi-satellite observation mission scheduling. Additionally, after the completion of observation, the results should be transmitted to the ground station in the shortest time. Due to the conflict in the visible time window between multi-satellite and limited ground stations, an efficient scheduling method is of great significance for timely completion of missions. There have been increasing research attentions on satellites mission scheduling problem in recent decades [3]. [PERSON] et al. considered various constraints in the EOS problem, including energy and priority. They proposed a planning method based on genetic algorithm and tested it on three different types of satellites [4]. [PERSON] et al. studied the multi-satellite redundant target planning problem and proposed a fast solution method based on complex networks [5]. [PERSON] et al. proposed a mode of high and low resolution payload satellites working together to solve the problem of multi-satellite cooperative observation [6]. [PERSON] et al. extended the adaptive large neighborhood search algorithm to the multi-agile satellite planning problem [7; 8]. As for area targets, the regional targets are usually divided first, and then arrange the observation tasks of each satellite [9]. The ground station scheduling(GSS) is a crucial part in satellite control and data delivery [10]. Once the satellite completes its mission, taking into account factors, such as data timeliness and onboard memory, the results need to be transmitted to ground users in a timely manner. GSS problem is extremely complex and has been proven to be NP-hard [11]. [PERSON] et al. considered the utilization rate of ground stations in the GSS problem and solved it with genetic algorithm [12]. [PERSON] et al.