In designing and scheduling batch plants, engineers strive to choose the ones that maximize economic benefits, subject to constraints imposed by process physics and market demands. Even for relatively small design projects and for experienced engineers, the number of practical alternatives is combinatorially explosive. The formalism of mathematical programming provides a convenient way of organizing alternatives and a rigorous justification of choices. Mathematical modeling of these design problems yields large mixed integer linear programs (MILPs). Thus, the effective we of optimization techniques to formalize the design process relies on the ability to solve large models. Distributed computing can be utilized effectively in the solution of MILPs modeling practical design and scheduling problems. This work demonstrates how large MILPs can be solved using a distributed computing platform. The viability of the distributed computing approach is illustrated by presenting results for problems derived from industrial design case studies.