An efficient algorithmic approach to the design and campaign planning of multipurpose plants performing nonlinear batch processes is presented. Nonlinear sequences of processing tasks involving batch mixing and splitting as well as multiple outputs from certain production steps are usually found in real industrial problems. Through the proper allocation of storage capacity, this work shows that a nonlinear batch network can be transformed into an equivalent set of simpler task sequences, called stages, as long as the stage precedence relationships are all fully satisfied. The proposed design procedure consists of the successive solution of the following three optimization problems : (1) a nonlinear mathematical programming (NLP) problem accounting for the stage precedence constraints and providing the set of multistage campaigns to be run, their lengths and the equipment capacity for each processing task all at once. Multiple execution of every single/multistage campaign is permitted by the formulation. Though the existence of a feasible sequencing of such campaigns is guaranteed by the problem, a linear STRIPS-like planner has subsequently been applied to efficiently find it. (2) a small-size mixed-integer linear programming (MILP) problem assigning a sufficient number of standard-size units working in-phase to each processing task. (3) a slightly modified version of the NLP-formulation adjusting stage batch sizes to save some of the assigned smaller units. Three example problems all involving nonlinear batch structures have successfully been solved using GAMS/MINOS 2.25 PC/386 version in a reasonable CPU time.