Fuel management is undertaken to mitigate the adverse consequences of wildfire. Finite mitigation budgets demand selective prioritization of forest stands for targeted fuel reduction treatments. A range of modeling methods have been used to identifiy optimal fuel treatment plans at various spatial and temporal scales of investigation; however, strategic analysis of fuel management alternatives can involve a range of limitations and challenges, including the prevalence of one-time solutions, static models lacking dynamic adaptability, and challenges in accounting for the stochastic nature of fire behaviour. To navigate these complexities, our study combines remote sensing-based analysis with a random search optimization algorithm to inform strategic fuel management and wildfire mitigation planning. For two communities in Alberta, Whitecourt and Hinton, we assessed landscape fire exposure within and around the built environment and rated hazardous fuels by the number of buildings they exposed (i.e., Building Exposure load, BEL). Through the assessment of BEL and the outcomes of the optimization algorithm, our model identified key areas for intervention, enabling a more informed allocation of mitigation resources. We found good alignment between expert-derived fuel treatment areas and our model-derived fuel reduction areas, PFRs, confirming the utility and relevance of our findings. The methodology is adaptable to diverse regional fuel characteristics and it also offers a phased implementation to assisting communities with financial constraints. The suggested systematic approach aids communities that lack local expertise in developing proactive fuel treatment strategies. Additionally, this study emphasizes the need to combine fuel treatment prioritization with community involvement, acknowledgment of potential local limitations, and financial planning to enhance its effectiveness and adaptability.