Consistently complying with end-point specifications, mainly end-use product properties, is a key issue to the competitiveness of batch processes. To maximize in some way the probability of observing successful runs, a series of experiments is specifically designed to pinpoint the smallest operating region that guarantees that end-point conditions can meet their desired targets. On the basis of data-driven modeling of the underlying binomial probability of success, the proposed methodology seeks to trade off improving parameter precision with experimenting in a reduced region where there is a high probability of satisfying end-point specifications. Two case studies are used to demonstrate the efficacy of the probability-based optimal design of experiments to find optimal policies for runs involving stochastic binary outcomes. Run-to-run improvement of the success rate for the operating policy in the acetoacetylation of pyrrole with diketene is first discussed. Results obtained for emulsion polymerization of styrene are also presented to illustrate how end-use properties, such as the tensile strength and melt index,, can be maintained in their desired target regions by the proper choice of the operating policy.