In industrial applications, many batch/repetitive systems are constructed by two or more subprocesses connected in serial to perform a given task repetitively or periodically, termed as cascaded batch/repetitive processes. In this paper, by introducing a two-dimensional (2D) cost function, an integrated design method of a novel cascade control scheme is developed for this kind of process under the 2D model predictive iterative learning control infrastructure. The resulted cascade control scheme essentially consists of a real-time cascade model predictive control along the time direction as the inner-loop control and an iterative learning control (ILC) along the cycle direction as the outer-loop control. The proposed design algorithm not only realizes an integrated design for all control loops but also guarantees optimal control in terms of the 2D control performance. The convergence criterion of the closed-loop control system along the cycle index is also investigated. Compared to the conventional cascade control schemes, the proposed algorithm is essentially a 2D feedback control with superior control performances which are clearly illustrated by the simulation results on the injection molding process.