Road transport is a major contributor to freight-related greenhouse gas emissions, and its relevance for efforts to decarbonize the transport sector as a whole is increasing. It is by now agreed that decarbonization of road freight will ultimately hinge on a transition away from oil-based fuels, mainly diesel and gasoline, which continue to dominate the sector. However, alternatives to oil span a multitude of technologies, ranging from electricity to biofuels, and entail varying levels and forms of investment. To support the development of informed decarbonization strategies in this context, we describe a bicriteria mathematical programming model for optimizing vehicle replacement decisions in a fleet of trucks to be operated over several years. Given a specification of the initial fleet, the model generates a set of renewal strategies that achieve different tradeoffs between aggregate well-to-wheel emissions and total investment and operation costs. The model captures heterogeneity across vehicle technologies, truck types, payloads, and operational profiles, while explicitly accounting for their implications for costs and emissions. The model also incorporates the installation costs of charging and alternative fueling infrastructure, of maintenance and insurance, as well as proceeds from salvage actions. Leveraging cost and emission data informed by French logistics operators, we investigate Pareto efficient strategies for the renewal of a fleet representative of real-world freight activity. The results reveal a clear cost--emissions tradeoff, with intermediate renewal strategies achieving substantial emission reductions before the sharply increasing marginal costs associated with full electrification are incurred. More broadly, the paper demonstrates how multi-objective, as opposed to single-objective, fleet renewal models make cost-emissions tradeoffs explicit and support the selection of decarbonization pathways simultaneously aligned with environmental goals and economic constraints.