As Earth Observation (EO) missions advance towards Agile Earth Observation Satellites, the complexity of scheduling problems increases, posing challenges for traditional optimization methods. This thesis investigates the potential of a quantum algorithm to address the scheduling problem in EO constellations. In particular, a novel formulation of the satellite constellation optimization problem is proposed, translating it into a Quadratic Unconstrained Binary Optimization (QUBO) problem, i.e., compliant with quantum solvers. Penalty functions are incorporated to optimize mission energy consumption. The formulated QUBO problem is then implemented and solved on a real quantum computer (a D-Wave Quantum Annealer). The performance provided by the quantum machine is compared with established classical meta-heuristic solvers like Simulated Annealing and Tabu Search.