Designing effective conservation strategies requires deciding not only where to locate conservation actions (i.e. which territorial units should be priortized), but also which type actions should be deployed. For most of conservation planning contexts, deciding where and what to do usually yields a complex and computationally challenging decision-making setting. Although the resulting optimization problems have typically been tackled using heuristic approaches, recent advances in mixed integer programming (MIP) solver technology have turned MIP-based approaches into a practical alternative for solving complex conservation planning problems. We introduce the R package prioriactions, which allows solving complex conservation planning problems comprising prioritization and action deployment decisions. prioriactions features a MIP approach that allows formulating and solving optimally (or nearly optimally) a wide class of conservation planning problems (characterized by different spatial and functional constraints and requirements). Furthermore, the package allows using a variety of commercial and open-source exact solvers enhancing its usability as well as its practical effectiveness. Here, we present a comprehensive description of the main functions available in prioriactions. This package has a workflow of three straightforward steps: (a) validation of the input data, using the inputData() function that prepares input; (b) the creation of a prioritization model, using the problem() function, allows the creation of two types of common models: the minimization of costs to achieve a recovery target and maximizing the recovery benefits given a limited budget; and (c) to solve of the model, using the solve() function. The prioriactions package provides a user-friendly platform for addressing different multi-actions management problems, allowing to identify more rigorously, transparently and in a reproducible way the spatial deployment of management actions.