While Large Language Models (LLMs) offer promising avenues for automated cognitive restructuring in mental health settings, current approaches predominantly focus on superficial positive reframing and lack the adaptability to balance conflicting therapeutic dimensions, such as empathy and rationality. To address these deficiencies, we propose CARE-CR, a context-aware framework that implements a decoupled optimization paradigm. We first train expert policies specialized for distinct therapeutic attributes rather than relying on a monolithic alignment strategy. To mitigate expert data scarcity, we introduce Dimension-Guided Hierarchical Monte Carlo Tree Search (DG-HMCTS) for data-efficient preference augmentation. At inference, a context-aware routing module dynamically predicts optimal preference weights to fuse expert outputs based on the user’s specific distress context. Extensive experiments demonstrate that CARE-CR achieves consistent improvements over strong baselines across multiple evaluation dimensions, including diagnostic accuracy, contextual appropriateness, task effectiveness, and overall helpfulness, while enabling controllable cognitive restructuring generation. The dataset and code are publicly available at https://github.com/HongzhiQ/CARE-CR.