The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start. In this work, Dynamic Programming is used to define the optimal energy management strategy for the REEV with a multi-objective cost function that takes into account not only fuel consumption, but also engine start/stops and pollutant emissions. To this aim, experimental data has been used to estimate emissions and develop a thermal model for the after-treatment system. Specifically, a Class 6 pick-up and delivery truck with a plug-in series-hybrid architecture has been modeled in a backward simulator using experimental performance maps. The results show that the optimal energy management strategy with respect to fuel consumption alone is a “blended” strategy. Conversely, the optimal strategy for minimum emissions and reduced start/stops is found to be a charge-depleting (pure electric) strategy with a one-time recharge. When the conflicting objectives of minimum fuel consumption, low number of engine start/stops, and reduced emissions are included in a single cost function for multi-objective optimization, the results show that a trade-off solution can be selected, for which the fuel consumption is near-optimal (less than 5% increase), the engine start/stops are low, and the pollutant emissions are reduced (by more than 50%).