The Global Strategy for Plant Conservation (GSPC) aims to protect 50% of the most important areas for plant diversity by 2010. This study selects sets of 1-degree grid cells for 37 sub-Saharan African countries on the basis of a large database of plant species distributions. We use two reserve selection algorithms that attempt to satisfy two of the criteria set by the GSPC. The grid cells selected as important plant cells (IPCs) are compared between algorithms and in terms of country and continental rankings between cells. The conservation value of the selected grid cells are then considered in relation to their future species complement given the predicted climate change in three future periods (2025, 2055, and 2085). This analysis uses predicted climate suitability for individual species from a previous modelling exercise. We find that a country-by-country conservation approach is suitable for capturing most, but not all, continentally IPCs. The complementarity-based reserve selection algorithms suggest conservation of a similar set of grid cells, suggesting that areas of high plant diversity and rarity may be well protected by a single pattern of conservation activity. Although climatic conditions are predicted to deteriorate for many species under predicted climate change, the cells selected by the algorithms are less affected by climate change predictions than non-selected cells. For the plant species that maintain areas of climatic suitability in the future, the selected set will include cells with climate that is highly suitable for the species in the future. The selected cells are also predicted to conserve a large proportion of the species richness remaining across the continent under climate change, despite the network of cells being less optimal in terms of future predicted distributions. Limitations to the modelling are discussed in relation to the policy implications for those implementing the GSPC. Keywords: Africa, Climate change, Global Strategy for Plant Conservation, important plant areas, persistence, reserve selection algorithms.