Behavior change technologies support a wide range of domains, including health, education, and habit formation. Despite their broad applicability, conventional intervention design relies on theory-specific assumptions and expert heuristics. These limitations reduce flexibility, obscure theoretical rationale, and hinder adaptation to dynamic user states (e.g., changes in motivation, ability, or environment) and deviations from intended behavioral trajectories. This study introduces a structured methodology for designing adaptive and personalized interventions. The approach integrates an ontology-based network of behavioral constructs, multi-objective optimization for strategy generation, and semantic explanation via large language models (LLMs). The system identifies relevant theoretical constructs through semantic similarity, formulates stage-wise intervention paths, and generates interpretable content aligned with user-specific trajectories. The proposed method offers three key advantages: reduced reliance on expert intuition through structured theory selection and staged planning; transparent, ontology-grounded explanations for intervention design; and continuous adaptation to evolving user states. Illustrative applications and simulations demonstrate the framework’s conceptual feasibility and design principles, positioning this work as an initial exploration rather than empirical validation.