Optimal design has been an under-utilized methodology. However, it has significant real-world applications, particularly in mixed methods implementation research. We review the concept and demonstrate how it can be used to assess the sensitivity of design decisions and balance competing needs. For observational studies, this methodology enables selection of the most informative study units. For experimental studies, it entails selecting and assigning study units to intervention conditions in the most informative manner. We blend optimal design methods with purposeful sampling to show how these two concepts balance competing needs when there are multiple study aims, a common situation in implementation research.