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Earth observation satellites (EOS) collect vital data for various applications such as weather forecasting, disaster management, environmental monitoring, etc. Maximizing the value of this data requires designing optimal EOS missions to capture targets with high business value or priority while satisfying complex constraints such as storage capacity, energy limits, weather, etc. However, traditional computing methods often struggle with the complexity of optimizing EOS mission schedules, leading to suboptimal target selection and reduced data collection efficiency. To address this challenge, there is a growing interest in leveraging quantum computing to enhance the efficiency and accuracy of EOS mission planning. Quantum computing provides the potential to explore vast solution spaces and find optimal schedules for EOS missions, even when faced with complex constraints and objectives. In this paper, we demonstrate the potential of our quantum algorithm to optimize EOS mission schedules and improve the efficiency of multiple EOS in real-time. The aim is to maximize the acquisition of high-priority targets with significant business value within the constraints of limited resources. We evaluated the performance of our quantum algorithm by comparing it with two classical optimization algorithms: simulated annealing and Gurobi optimizer. Our quantum algorithm outperformed the Gurobi optimizer by 23.46% in selecting high-priority targets, while satisfying all constraints. Although the simulated annealing executed faster than the quantum algorithm, its accuracy in providing high-value targets was poor in comparison. Moreover, the Gurobi …