Conventional randomized clinical trials (RCTs) compare treatment effectiveness to provide support for evidence-based treatments that can be generalized to the average patient. However, the information obtained from RCTs may not always be useful for selecting the best treatment for individual patients. This article presents a complementary approach to identifying optimized treatments using experimental designs that focus on individuals. Personalized, or N-of-1, designs provide both a comparative analysis of treatments and a functional analysis demonstrating that changes in patient symptoms are likely because of the treatment implemented. This approach contributes to the zeitgeist of personalized medicine and provides clinicians with a paradigm for investigating optimal treatments for rare diseases for which RCTs are not always feasible, identifying personally effective treatments for patients with comorbidities who have historically been excluded from most RCTs, handling clinical situations in which patients respond idiosyncratically (either positively or negatively) to treatment, and shortening the time lag between identification and implementation of an evidence-based treatment. These designs merge experimental analysis of behavior methods used for decades in psychology with new methodological and statistical advances to assess significance levels of changes in individual patients, and they can be generalized to larger populations for meta-analytic purposes. This article presents a case for why these models are needed, an overview of how to apply personalized designs for different types of clinical scenarios, and a brief discussion of challenges associated with interpretation and implementation of personalized designs. The goal is to empower pediatricians to take personalized trial designs into clinical practice to identify optimal treatments for their patients.