This study describes a general framework for coupling and optimizing multiple models with multiplier-free reduced Hessian successive quadratic programming. This tailored approach enables the use of existing process simulators/models simultaneously with the optimizer, and this leads to efficient solution of process optimization problems. In this paper, a unified strategy is proposed to combine the model information and then decompose the sensitivity matrices that arise from the connections of the streams. With the proposed decomposition approach, the need for storing a large constraint matrix is avoided and individual models that only pass their Newton corrections and sensitivity information are left. The solution avoids problems due to model failure and can save a lot of time because models are not converged at intermediate optimization iterations. The resulting tailored optimization approach is illustrated on the dynamic optimization of a batch reactor/column system selected from Yi and Luyben (Comput. Chem. Eng. 1997, 12, 25).