In this paper, a systematic methodology is presented for the deterministic optimization of batch processes under uncertainty. The methodology is based on the use of classical Monte Carlo simulation in order to evaluate the objective function and the process constraints together with their analytical derivatives with respect to the optimization parameters. A deterministic, derivative-based optimization algorithm can then be used to locate the optimum values of the optimization parameters. The main advantage of the proposed methodology stems from the fact that the size of the resulting optimization problem is the same as that of the nominal (without uncertainty) optimization problem and it is independent of the number of uncertain parameters. Three examples, involving a batch chemical reactor, a batch distillation column, and a batch polymerization reactor, are presented to illustrate the usefulness of the proposed methodology.