Personal scheduling differs from industrial scheduling by emphasizing user time and incorporating human-centric factors, such as energy levels and individual preferences. To address the limitations of existing automated scheduling systems, we developed TaskFlow Optimizer, a constraint programming framework leveraging the CP-SAT solver from Google OR-Tools. The framework provides a library of customizable soft constraint templates, encompassing energy management, task grouping, break scheduling, and deadline adherence. Extending classical scheduling theory, the model integrates user-specific parameters through a weighted objective function, ensuring mathematically optimal solutions. Its modular architecture facilitates adaptation to diverse application scenarios.