Recent advancements in 18F radiochemistry, such as the advent of copper-mediated radiofluorination (CMRF) chemistry, have provided unprecedented access to novel chemically diverse PET probes; however, these multicomponent reactions have come with a new set of complex optimization problems. Design of experiments (DoE) is a statistical approach to process optimization that is used across a variety of industries. It possesses a number of advantages over the traditionally employed "one variable at a time" (OVAT) approach, such as increased experimental efficiency as well as an ability to resolve factor interactions and provide detailed maps of a process's behavior. Here we demonstrate the utility of DoE to the development and optimization of new radiochemical methodologies and novel PET tracer synthesis. Using DoE to construct experimentally efficient factor screening and optimization studies, we were able to identify critical factors and model their behavior with more than two-fold greater experimental efficiency than the traditional OVAT approach. Additionally, the use of DoE allowed us to glean new insights into the behavior of the CMRF of a number of arylstannane precursors. This information has guided our decision-making efforts while developing efficient reaction conditions that suit the unique process requirements of 18F PET tracer synthesis.