In order to solve the problem that observational image quality and profits are low in satellite scheduling by adopting lagged weather forecast cloud information, a mathematic model capturing real-time cloud distribution was proposed. The Agile Earth Observation Satellite (AEOS) scheduling model was also built based on the real-time cloud information. Considering the local optimization of Greedy Search Algorithm (GSA) and it can give full consideration for constraints such as cloud of satellite observation and limited storage resources, the applications of GSA for the satellite scheduling problem were researched. Firstly, the cloud coverage of observation task was considered in priority order by GSA. The image quality value of observation task was calculated according to the size of cloud coverage and the observation task was selected by the sort of the image quality value. Secondly, the task with the maximize profit was selected according to task size, deadline and satellite storage resource. Finally, satellite observation and task transmission were completed according to their ability of improving profit. The simulation experiments show that, on the case of 100 tasks, the task profit of satellite schedule adopting GSA was improved by 14.82% and 10.32% compared with the Dynamic Programming Algorithm (DPA) and Local Search Algorithm (LSA) respectively. Besides, the image quality of applying GSA is higher than taking DPA and LSA in the same circumstance. The experimental results show that the GSA can effectively improve the image observation quality and task observation profit of satellite scheduling.