In this paper, the energy saving effect of optimized driving strategy is presented and compared to human driving strategy. The driving strategy of a one-seated experimental electric vehicle is investigated and optimized in this study, where the objective function of optimization is the minimization of the consumed energy. Measurement-based vehicle model is used during the optimization process. The initialization and constraints of optimization are set up by analyzing the acquired vehicle data of the driver. The analyzation is done using a transform algorithm, making the initialization of optimization automated. Genetic algorithm is used with mixed initial population acquired from measured driving data and from creation function. Using this hybrid initial population helped to decrease the time of optimization. The resulted velocity profile of the optimized driving strategy was used in field test measurements, where 4.28 % energy savings was achieved compared to the results prior to optimization.