Satellite imaging has become very popular in recent years. Nanosatellites have been attracting growing interest as they proved to be a good alternative for the realisation of missions with the aim to monitor the environment from space and to acquire image data from every place on Earth. In spite of the short revisit time that is achieved by existing satellite constellations, a method was developed for the digital determination of the orbital manoeuvres for single nanosatellites. The aim of the analysis was to study the possibilities of the optimisation of orbital operations in the context of maximising the operational efficiency of the mission. The conducted experiments involved the assessment of various scenarios of orbital manoeuvres taking into consideration the key limitations, such as the available fuel weight, propulsion efficiency, and the requirements concerning time and energy. The obtained results revealed that the most efficient manoeuvres are those that balance the minimum consumption of fuel or energy with the maximum extension of the duration of the satellite’s stay above the area of interest. For example, slight adjustments to the altitude of the orbit with the use of Hohmann transfer proved to be optimal in terms of fuel costs. On the other hand, changes in inclination, although they are definitely energy-consuming, may significantly improve the coverage of the defined area. The conclusions from the conducted analyses confirmed that an appropriate strategy of orbital manoeuvres may greatly improve the operational efficiency of the nanosatellite, while, at the same time, continuing to save fuel and energy. It is suggested that future research should develop towards more advanced optimisation techniques, such as artificial intelligence algorithms that may additionally improve the precision and efficiency of planning orbital trajectories.

Earth observation satellite constellations must ensure rapid and reliable coverage of critical areas under propulsion constraints and environmental variability. Existing studies typically treat constellation geometry, maneuver optimization, and operational assessment as separate problems, limiting understanding of the structural origin of performance constraints. This study proposes a three-stage hierarchical optimization framework that separates intrinsic geometric potential, reactive multi-objective maneuver adaptability, and stochastic operational realizability. In Stage 1, a genetic algorithm optimizes constellation geometry with respect to global success rate across multiple areas of interest. In Stage 2, NSGA-II-based maneuver optimization evaluates the trade-off between success rate, time to first access, and total $\mathsf{\Delta }V$ expenditure. In Stage 3, operational performance is assessed using the Operational Imaging Response Time (OIRT) metric under probabilistic environmental filtering. Simulation results show that constellation geometry is the primary structural determinant of performance within the analyzed configuration range, while maneuver optimization provides moderate but measurable response-time improvements. Environmental filtering reduces achievable coverage relative to purely geometric predictions. The proposed framework establishes a consistent methodology linking constellation synthesis, maneuver planning, and operational response evaluation for time-critical Earth observation missions.