Radiochemical reaction optimization is a central yet time and resource-intensive task within the field of radiopharmaceutical chemistry. Optimization studies are used to understand new radiochemical methodologies to better apply them to relevant radiosynthetic problems. Once a radiotracer candidate is earmarked for production, the radiosynthesis needs to be well-optimized to ensure the procedure’s robustness, efficiency, and performance. A detailed understanding of a radiochemical process, obtained through rigorous optimization studies, can help guide decision-making during synthesis automation, increase margins for error during critical steps, and allow one to identify critical factors that may impact product yield or quality. “Design of experiments” (DoE) is a statistical approach to process optimization and has been applied for decades in multiple process industries. DoE studies are designed to select a “statistically optimal” set of experiments, the data from which can be used to model multivariate experimental space with excellent experimental efficiency. This chapter aims to serve as an introductory overview of the DoE approach to process optimization within the context of the radiopharmaceutical sciences. Additionally, it will discuss the potential of DoE to help better study, understand, and apply new radiochemical methodologies, as well as facilitate the development and manufacture of novel radiopharmaceuticals.