The improvement of the efficiency of vehicle energy systems promotes an active search to find innovative solutions during the design process. Engineers can use computer-aided processes to find automatically the best design solutions. This kind of approach named "multi-objective optimization" is based on genetic algorithms. The idea is to obtain simultaneously a population of possible design solutions corresponding to the most efficient energy system definition for a vehicle. These solutions will be optimal from technical and economic point of view. In this article this kind of "genetic intelligence" is tested for the holistic design of the optimal vehicle powertrain solutions and their optimal operating strategies. The methodology is applied on D class hybrid electric vehicles, in order to define the powertrain configurations, to estimate the cost of the powertrain equipment and to show the environmental impact of the technical choices. The optimal designs and operating strategies are researched for different vehicle usages - normalized, urban and long way driving. (C) 2015 Elsevier Ltd. All rights reserved.