<p>Spatial management planning demands integration of strategic multi-criteria prioritization with tactical resource allocation–a challenge often addressed separately in prior work. We propose a framework that couples these levels via Multi-Attribute Utility Theory (MAUT) and Mixed Integer Programming (MIP). MAUT translates stakeholder-informed preferences into utility functions across multiple criteria, while MIP optimizes spatially explicit, cost-effective management plans under technical and operational constraints. A distinctive feature is that management actions are prioritized by the utility change they generate, rather than by zone rankings alone, creating a direct link between stakeholder preferences and tactical allocation. An application to forest fuel management for wildfire risk reduction is presented, showing how the framework prioritizes treatments according to their transformative efficiency. Results demonstrate optimality and computational feasibility for landscape-scale problems, with flexibility across diverse management scenarios. By bridging strategic prioritization and tactical implementation, this framework addresses a critical gap in spatial decision support, offering a replicable approach for integrated planning in conservation and natural resource management.</p>