The problem of earth observation satellites planning and scheduling contains many objectives and constraints. The objectives include downloaded data, profit of scheduled tasks and quality (weight) values. The constraints are related to energy, data, targets (tasks) and ground stations. Many optimization methods have been used to optimize this problem such as dynamic programming (DP), simulated annealing (SA), ant colony optimization (ACO), genetic algorithm (GA) and constraint programming approach (CPA). Each reported research used a harmonized combination of objective(s), constrains, and optimization techniques. The presented work investigates the anatomy of this optimization problem. It is also analyzes the findings of the previous relation between the optimization problem elements and gaps in past research. Furthermore shows that there is a gap between data's objectives (maximizing the total amount of downloaded data) and the targets' constraints (observation of tasks, scheduling and consecutive observation).The most widely used optimization methods are dynamic programming and heuristic algorithms, respectively. Moreover, the most widely objectives are those relative to profit. Few discussions are also presented concerning the constraints which involve data rate, range of ground stations and capacity of ground stations.