Multimorbidity, the co-occurrence of multiple physical or psychological illnesses, is prevalent particularly among older adults. The number of Americans with multiple chronic diseases is projected to increase from 57 million in 2000 to 81 million in 2020. However, behavioral medicine and health psychology, while focusing on the co-occurrence of psychological/psychiatric disorders with primary medical morbidities, have historically tended to ignore the co-occurrence of primary medical comorbidities, such as diabetes and cancer, and their biopsychosocial implications. This approach may hinder our ecologically valid understanding of the etiology, prevention, and treatment for individual patients with multimorbidity. In this selective review, we propose a heuristic behavioral framework for the etiology of multimorbidity. More acknowledgment and systematic research on multiple, co-existing disorders in behavioral medicine are consistent with the biopsychosocial model's emphasis on treating the "whole person," which means not considering any single illness, its symptoms, risk factors, or mechanisms, in isolation. As systems analytics, big data, machine learning, and mixed-model trajectory analyses, among others, come online and become more widely available, we may be able to tackle multimorbidity more holistically, efficiently, and satisfactorily.