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Applying ecosystem service valuation principles to natural resources management has the potential to encourage the efficient use of resources, but can decision support systems built on these principles be made both practical and robust? The limitations to building such systems are the practical limits on managers time to develop or learn tools and the state of the science to support decision-making components. We address this question by applying a cost-effectiveness analysis framework and optimization model to support the targeting of restoration funds to control an invasive grass (Bromus tectorum) in agro-ecosystems. The optimization aims to maximize benefits derived from a suite of ecosystem services that may be enhanced through site restoration. The model combines a spatially-varying cost function with ecosystem service benefit functions that are risk-adjusted to capture the probability of successful restoration. We demonstrate that our approach generates roughly three times the level of ecosystem service benefits (as measured through indicators) compared to the current management strategy of selecting restoration sites that are superlative producers of one ecosystem service. The results showed that spatial (GIS) data and ecosystem understanding were sufficient to formally capture the managers informal decisions and that cost-effectiveness of restoration could be improved by considering the ability of sites to jointly produce multiple ecosystem services and adjusting expected benefits by the probability of success.