Acquiring remote sensing information from outer space by imaging satellite is the major task of Earth Observation Satellite, which plays increasingly important roles in social, scientific, and economic activities. In recent years, more and more imaging satellites are launched to meet various application requirements, which challenged the satellite ground application system to the united mission scheduling for imaging satellites. United mission scheduling is the decision-making process of choosing the pending imaging requests under the imaging constraints of each satellite. The motivation of united mission scheduling is to get the most cost-effective imaging results and make full use of the satellite resources. Currently, the united mission scheduling research is still in its initial stage with many open problems in theory and practice needs to be tackled.This thesis focuses on the technique issues of united mission scheduling, including scheduling mode, data preprocessing, problem model, optimal algorithm, and etc. The main work and contribution are as follows:1. Based on analysis of satellite imaging procedure, imaging constraints, satellite ground management flow, and evolutionary multi-criterion optimization techniques, we propose two united mission scheduling strategy—the global optimization and the two-step optimization.2. For the global optimization of the united mission scheduling, we build a multi-objective model according to imaging constraints. On the basis of this model, the thesis proposes a multi-objective mission scheduling algorithm by designing 0-1 constant length encoding method and imaging constraint genetic operators. The performance of the algorithm is verified by theoretical analysis and experiments. Results show that it can solve the united mission scheduling problem of different scales effectively.3. Under the two-step optimization strategy, we construct a directed graph model and propose an imaging requests pre-scheduling approach, which converts united mission scheduling problem to that of each involved single imaging satellites. Based on the NSGA2, we propose a multi-objective imaging path search algorithm to solve the mission scheduling problem of each imaging satellite. Natural coding technique and constraints satisfaction genetic operator are used to search the optimal imaging paths. The experiment shows that the proposed algorithm can solve united mission scheduling effectively.4. We analyzed the characteristics of mission scheduling in emergent condition under the two-step optimization strategy. By applying the concept of delay dominant path, we propose a multi-objective label-setting path search algorithm which can quickly generate the Pareto optimal solutions.We have conducted experiments on the proposed model and algorithms to validate their effectiveness. Some of our research achievements are applied to several mission scheduling systems which show approving results.