Abstract: A comprehensive study is conducted on the design methodology for low Earth orbit (LEO) mega-constellations composed of heterogeneous satellites with diverse functions, orbits and payloads. Three critical challenges are addressed, including nonlinear coverage superposition, differentially coupled drift trajectories, and large-parameter optimization. An efficient semi-major axis decay model is developed under the influence of J₂ perturbation and atmospheric drag with combined linear and quadratic fitting. Relative drift compensation equations for both the right ascension of the ascending node and the argument of latitude are derived to maintain orbital stability. A complex optimization model is constructed with the objective of minimizing the integrated manufacturing-launch cost, incorporating multi-dimensional coverage constraints such as revisit time, imaging resolution and coverage stability as well as multi-source engineering constraints including limited fuel, selectable satellites, and multiple mission phases. Key variables are reduced through theoretical analysis to improve computational efficiency. A genetic algorithm is employed to determine the cost-optimal nominal configuration parameters. Furthermore, a closed-loop design framework is developed to coordinate nominal configuration, partial correction, fuel planning, and control implementation throughout the constellation lifecycle. For missions targeting coverage between 30°(N) and the equator, with constraints of ≤10min revisit time and ≤1m imaging resolution, the optimized heterogeneous constellation configuration demonstrates a 26%～30% reduction in total cost compared with uniform configurations. Additionally, temporal consistency of revisit time across different latitudes shows improvement. The performance advantage increases with wider latitude coverage. The optimized methodology enables synergistic optimization of coverage performance and cost, supports scalable design of constellations exceeding 1000 satellites for emergency remote sensing and global IoT applications, and contributes to reduced lifecycle cost and design complexity. 

Key words: heterogeneous constellation, configuration optimization, genetic algorithm, coverage performance, integrated cost, full-process design