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Abstract: Conserving nature in the presence of humans is especially challenging in areas where livelihoods are largely based on locally available natural resources. The restoration of forests in such contexts calls for the identification of sites and actions that improve biodiversity protection, and ensure the provision of and accessibility to other forest-related ecosystem services. This paper introduces an integer-linear programming (ILP) approach to identify reforestation priorities that achieve such goals. Applications of ILP to nature conservation are many, but only a few of them deal with the problem of restoration, and none of the available models considers the basic needs of the local population. Given constraints on a restoration budget, the potential conversion of productive lands and the travel time to reach harvestable forest, the model maximises the amount of reforestation area (weighted by priority values) and minimises the harvesting of existing forest, while ensuring the conservation of landscape diversity, the satisfaction of timber demands and the stabilisation of erosion-prone land. As an input, suitability maps, generated through a combination of ecological criteria, are used to prioritise the selection of reforestation sites. An application to a 430 km2 area in Central Chiapas (Mexico) resulted in compact patches and thus a manageable reforestation plan. Acceptable trade-offs were found between the amount of soil stabilisation possible and the prioritisation goals, while uncertainty in the prioritisation scores did not significantly affect the results. We show that restoration actions can be spatially designed to benefit both nature and people with minimal losses on both sides. [Copyright &y& Elsevier]