This paper presents a methodology to identify energy efficient driving strategies to optimize the longitudinal dynamics of vehicles. The methodology is described in detail for two different driving situations. For these situations data from driving tests of subjects is examined and used to compare the energy efficient driving strategies to the average behaviour of subjects. Results of the analysis of driving situations are applied to a complete reference course. Simulations of this reference course identify a 21 % reduction of fuel consumption by applying efficient driving strategies. This reveals that the optimization of the longitudinal dynamics of vehicles can yield great savings in energy consumption. General requirements for a deployment of the proposed strategies to a driver assistance system are discussed. The methodology described in this paper considers deviations of fuel efficient strategies from average driver behaviour. In case assistance systems such as adaptive cruise control automatically handle the longitudinal dynamics the decision to pursue energy efficient strategies can be taken by the system. Thus assistance systems can implement driving profiles that deviate from the average driving behaviour and are hence critical with respect to driver acceptance. Many publications are prone to completely focus on energy efficiency and do not consider driver acceptance appropriately. A holistic approach, which is partly presented in this document, which considers driver acceptance in the selection of viable driving strategies, can accomplish to reap the maximum reduction of energy consumption in traffic. Driver acceptance is a necessary precondition for the implementation and deployment of energy efficient driving strategies in real world traffic. The proposed methodology applied on simulation data in this paper provides positive results in simulation, which can be regarded as a first step for research on energy efficient longitudinal control systems. In a second step the energy efficient longitudinal dynamics control algorithms need to be implemented into a test vehicle. Measurement data from real world tests have to verify the described results.