Utilizing off-the-shelf components, this closed-loop system carries out parallel autonomous process optimization experiments in batch. The MAP is able to optimize continuous and categorical parameters for a reaction that is ubiquitous in pharmaceutical synthesis: the Suzuki-Miyaura cross-coupling. A key challenge addressed by this MAP is that categorical parameter selection has traditionally relied on chemical intuition, which can introduce bias in the experimental design. Our approach enables the definition of a set of meaningful, broad, and unbiased process parameters to drastically optimize our selected reaction. This system can easily be replicated to solve a multitude of multivariate process optimization problems. Once widely adopted, the technology has the potential to empower modern-day researchers to shift their focus away from routine experimental execution and toward higher-complexity problem-solving. This equipment for this MAP is provided by Chemspeed and Agilent.