Research sponsored by the US Department of Energy (DOE) aims to facilitate a clean and independent energy future for the nation. Strategic planning for energy research and development (R & D) can be complex and dynamic, in part due to federal budgetary constraints and volatility. Managing R & D funding to advance energy technologies, in spite of these challenges, is a crucial component of the nation's long term energy policy. This study demonstrates a portfolio decision analysis (PDA) approach to support R & D resource allocation decisions for the DOE Office of Fossil Energy's Carbon Capture and Storage R & D program. A multi-attribute value model uses technology readiness levels (TRU) and other metrics to represent the overall objectives of the R&D program in order to evaluate alternative research portfolios given limited funding. Mathematical optimization identifies efficient funding allocations for each technology program area to maximize the multi-attribute value generated from the total budget. This is especially useful for responding to externally-imposed-budget changes. As the case study demonstrates, explicitly funding the most value-generating options leads to greater expected R&D programmatic value than typical strategies of equal or proportional distributions of a budget change among technology program areas.