The complexity and habitability characteristics of exoplanet atmospheres provide unique inspiration for optimization algorithms. This paper proposes a heuristic optimization algorithm based on exoplanet atmospheric dynamics, chemical reactions, and orbital evolution mechanisms, called EAHOA (Exoplanet Atmospheres and Habitability Optimization Algorithm). This algorithm treats candidate solutions as planetary state vectors and combines adaptive greenhouse feedback, ternary chemical coupling, orbital-radiative co-perturbation, and cloud and atmospheric thickness adjustment mechanisms to explore optimal solutions in a multidimensional search space. This paper focuses on the algorithm's mechanism design, mathematical model, and theoretical analysis, demonstrating its unique dynamic adaptability and complex nonlinear characteristics. This algorithm provides a new theoretical framework for high-dimensional multi-objective optimization problems and can be applied to other heuristic optimization fields.