The mining industry is a significant contributor to greenhouse gas emissions and operational costs. The transportation of materials within mining operations, especially using diesel-powered trucks, accounts for a substantial portion of both emissions and costs. Optimizing fuel consumption in this context is crucial for environmental sustainability and financial efficiency. While there is some existing research on minimizing fuel consumption in mining operations, most of these studies prioritize production over fuel efficiency. Additionally, there is a lack of solutions addressing real-time dispatch problems while also minimizing fuel consumption. This thesis proposes the development of a model to optimize truck speeds in mining operations, with the primary goal of reducing fuel consumption. The focus is on minimizing waiting times for trucks at loading stations by adjusting their speeds using a metaheuristic algorithm, specifically Simulated Annealing (SA). To achieve this, a discrete event simulation (DES) model made in HaulSim to replicate a simplified mining site in Nevada, USA. The simulation includes Caterpillar 793 series trucks. The SA algorithm is applied to find optimal truck speeds, with the aim of reducing fuel consumption with the same level of production. The hypothesis is that lowering truck speeds in mining environments with queueing trucks can significantly reduce fuel consumption. The study explores how speed optimization impacts efficiency across different mining conditions, identifies the most effective optimization techniques, and quantifies potential fuel savings. The research reveals a 27-32% reduction in fuel consumption across various truck scenarios. However, these findings should be interpreted cautiously due to limitations in data constraints and modelling simplifications. In conclusion, the study highlights the effectiveness of SA in optimizing truck speeds and the potential for substantial fuel savings in mining operations. We recommend the integration of Artificial Neural Networks (ANNs) for a more nuanced approach to fuel consumption estimation and optimization, considering factors like road maintenance intervals and payload. This synergistic approach, combining metaheuristics and machine learning techniques, aligns with sustainable practices in the mining industry and offers promising avenues for further research and application.