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Mining is a capital-intensive industry that requires hundreds of million-dollar investment in major equipment. In regards to surface mining operations, mine trucks are the most common pieces of equipment that are used for material haulage. Their maintenance cost, however, constitutes a significant proportion of the overall operational cost. Currently available costing methods and models do not take into account all key constraints and as a result, maintenance cost cannot be minimised. A new mixed integer programming (MIP) model is developed to minimise the maintenance cost for a heterogeneous truck fleet over a multi-year period while considering a new truck-purchase option. The proposed model is applied to truck maintenance cost data from a gold mine in Western Australia. Results indicate 21?64% and 14?76% cost savings over 10 years in comparison to the spread sheet based and original MIP models, respectively.