The growing demand for scalable mental health support has increased interest in AI-based counseling systems grounded in Motivational Interviewing (MI). However, existing MI datasets do not explicitly model the structured progression of MI phases, which is essential for effective and goal-oriented counseling. To address this gap, we introduce PhaseMI, a phase-structured MI dataset, together with a data generation framework that employs therapist, client, and supervisor LLMs to explicitly control phase transitions. Compared to the best alternative baseline, PhaseMI achieves improved coverage of MI phases, with gains of 12.3% in exploring, 37.6% in guiding, and 61.1% in choosing, and experimental evaluations demonstrate that it yields higher overall counseling quality than baseline datasets.