The need to reduce pollutant emissions has pushed the automotive industry towards sustainable mobility promoting new technological solutions, among which the use of hybrid powertrains stands out. The development of a hybrid architecture is very complex and demands proper components sizing and the determination of optimized power-split strategies among different power sources, for example: Internal Combustion Engine (ICE), electric generator/motor and batteries. Moreover, the experimental analysis regarding performance and emissions requires that the whole propulsive system must be set up on the test bench, hence, negatively affecting the cost of the entire design phase. In this scenario, an optimum design and sizing approach for a series-hybrid electric vehicle (S-HEV) is proposed aiming at a design cost reduction. The presented procedure relies on numerical modelling of the hybrid powertrain and on the optimization of the fuel consumption and the driving range. The series-hybrid architecture model is referred to a front-wheel-drive vehicle. It is developed by modelling the longitudinal dynamics, the tyre grip control, the driveline and the power generation system. An Energy Management System (EMS) is adopted to manage the power fluxes among the on-board power devices, according to the imposed mission requirements. The model also includes a braking energy recovery mode allowing the battery to be partially recharged during the mission. The application of a Genetic Algorithm for optimization leads to an improvement in the overall efficiency of the powertrain. A commercially available series-hybrid vehicle is analysed as a representative case study, considering the use of a diesel engine rather than a petrol engine. The simulations were performed by using the New European Driving Cycle (NEDC). From this design stage, the evaluation of emissions and the performance optimization for the hybrid configuration can be conducted through experimental measurements on engine test benches, providing a more cost-effective and simpler assessment method.