Multi-Objective Spatial Planning // Provides a modular framework for exact multi-objective spatial planning using mixed-integer programming. The package supports the definition of planning problems through planning units, features, management actions, action effects, spatial relations, targets, constraints, and objective functions. It enables the optimisation of spatial planning portfolios under considerations such as boundary structure, connectivity, and fragmentation. Supported multi- objective methods include weighted-sum aggregation, epsilon- constraint, and the augmented epsilon-constraint method. Problems can be solved with several commercial and open-source optimisation solvers. Optional solver backends include the 'gurobi' R package, which is distributed with the Gurobi Optimizer installation https://docs.gurobi.com/projects/optimiz er/en/13.0/reference/r/setup.html, and the 'rcbc' R package, available from GitHub at https://github.com/dirkschumacher/rcbc. For background on multi-objective optimisation methods, see Halffmann et al. (2022) doi:10.1002/mcda.1780; for the augmented epsilon- constraint method, see Mavrotas (2009) doi:10.1016/j.amc.2009.03.037.