The batch process plays an important role in industrial production. Among them, production processes that have specific requirements for operational continuity in each processing stage should consider the no-wait constraint to meet the production reality. The batch process with a no-wait constraint is a typical NP-hard problem. In this work, we propose a framework for the integration of planning, scheduling, and control. We also propose a decomposition method with an improved genetic algorithm to solve the integration problem of scheduling and control for the no-wait batch process. The integrated formulation represents a typical mixed-logic dynamic optimization (MLDO) problem, which involves logical disjunctions and operational dynamics. Then, we address the integrated problem as a grey-box optimization problem, using data-driven feasibility analysis and surrogate models to approximate the unknown black-box constraints. Finally, we test specific production instances to demonstrate the feasibility and superiority of the proposed integration model of the no-wait batch process and optimization algorithm.