This paper presents a new approach to building a decision model for government funding agencies, such as the U.S. Department of Energy (DOE) solar office, to evaluate solar research funding strategies. High solar project costs - including technology costs, such as modules, and soft costs, such as permitting - currently hinder many installations; project cost reduction could lead to a lower project levelized cost of energy (LCOE) and in turn, higher installation rates. Government R&D funding is a crucial driver to solar industry growth and potential cost reduction; however, DOE solar funding has not aligned with the priorities for LCOE reduction. Solar technology has received significantly higher research funding from the DOE compared to soft costs. Increased research funding to soft cost programs could spur needed innovation and accelerate cost reduction for the industry. To this end, we build a cost model to calculate the LCOE of a utility-scale solar development using technology and soft costs and conduct a sensitivity analysis to quantify how the inputs influence the LCOE. Using these results, we develop a multi-attribute value function and evaluate six funding strategies as possible alternatives. We find the strategy based on current DOE allocations results in the lowest calculated value and the strategy that prioritizes soft cost results in the highest calculated value, suggesting alternative ways for government solar agencies to prioritize R&D funding and potentially spur future cost reduction.