The large number and significant variety of systems available for space exploration missions produce countless potential architecture combinations. Compounding this are the scheduling intricacies of system life-cycle phases, time dependent operational dependencies, as well as the uncertainty associated with each system and technology in terms of cost, schedule, and performance. Traditional architecting emphasizes the individual design of component systems over the wide-ranging and robust assessment of architecture options early in mission design. A top down method that can assess the capabilities, requirements, and risks associated with the diversity of available space systems and form optimal portfolios of interdependent systems is necessary. This dissertation describes and demonstrates a portfolio optimization technique that can de-sign and assess Lunar space exploration architectures by optimizing on programmatic objectives such as cost, performance, schedule, and robustness while simultaneously accounting for system operational interdependencies and schedule dependencies of the selected systems. Several specific enhancements to the Robust Portfolio Optimization method are produced, resulting in the the novel Progarmamtic Portfolio Optimization (PPO) approach: including life-cycle phase modeling, variable capability sizing of systems, and multi-domain constraints to model time dependent objectives.<br>