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Ecological restoration is essential to meeting global biodiversity conservation goals. Given limited conservation budgets, deciding where to restore habitat is a key challenge for the coming decade. We developed a spatially explicit framework to optimize ecological restoration site selection by integrating land use history, species distributions, and economic costs. The framework includes the following steps: identify potential restoration area based on relevant environmental measures like land use; identify species of interest; calculate restoration benefits by modeling habitat and climate suitability and estimating reduced extinction risk associated with restoring a particular land parcel based on a modified species-area relationship; aggregate benefits across species; and compare to parcel-level land acquisition costs. We applied linear programming to maximize conservation benefit/restoration cost ratios to identify optimized restoration sites. We illustrate this approach using a case study for highly threatened grassland ecosystems in the Great Plains region of Kansas, USA. We selected five grassland animal species (greater prairie chickens [Tympanuchus cupido], lesser prairie chickens [Tympanuchus pallidicinctus], swift fox [Vulpes velox], pronghorn [Antilocapra americana], and regal fritillary [Speyeria idalia]) as indicators of restoration benefit across taxa. For the indicator species that we chose, shortgrass and mixed-grass prairies had the highest conservation benefit to cost ratio. Setting a minimum restoration threshold for each habitat type allowed us to identify high-priority tallgrass prairie sites. Despite increasing interest in ecological restoration, optimizing restoration site selection is challenging because one must consider habitat features that do not currently exist. The modeling approach described here is flexible and can be updated for different ecosystems, species, and conservation priorities. We outline potential alterations that can be made in future analyses, depending on desired restoration goals.