Strong coupling of the physical and control parts within complex dynamic systems should be addressed by integrated design approaches that can manage such interactions. Otherwise, the final solution will be suboptimal or even infeasible. Combined design and control (co-design) methods can tackle this issue by managing the mentioned interactions and can result in superior optimal solutions. Current co-design methods are applicable to simplified non-interconnected systems; however, these methods might be impractical or even impossible to apply to real-world interconnected dynamic systems, hindering designers from obtaining the system-level optimal solutions. This work addresses this issue by developing an optimization algorithm which combines a decomposition-based optimization strategy known as analytical target cascading (ATC) with a co-design-centric formulation of multidisciplinary dynamic system design optimization (MDSDO). Considering the time-dependent linking variables among the dynamic systems’ components, a new consistency measure has also been proposed to manage such quantities in the optimization process. Finally, a plug-in hybrid electric vehicle powertrain, representative of an interconnected dynamic system, has been studied to validate the new algorithm’s results against the conventional all-at-once (AAO) MDSDO. Although the numerical results from the ATC-MDSDO slightly deviate from those in the AAO-MDSDO, this method can play a crucial role as a benchmark when the AAO solution is unattainable or a distributed design paradigm is required.