The concept of the sustainable development is closely related to the sustainable decision making in the field of energy. Due to the counteraction of the three pillars of sustainability, which are the environment, the society and the economy, any decision we choose will harm, more or less the balance of the sustainable system. The reason for this is related to the fact, that it is not possible to reach all environmental, social and economical preferences with the same priority at the same time. Therefore the purpose of this research is to assess the environmental and social compatibilities with respect to the use of renewable energy in future energy mix scenarios. Nowadays optimization problems in the energy sector mostly focus the minimization of energy service costs. As the lack of environmental and social issue optimization related to commercial and industrial energy use can cause unpredictable high expenses in environmental or social fields, the main focus is to develop a methodology to optimize the use of renewable energy use regarding the socio-environmental compatibility. This leads us to multi criteria decision making problems (MCDM) as well as to multi objective optimization problems (MOP). After defining the optimization criteria, the next step is the assessment of existing MOP methods. The focus will be on the classical MOP methods, because they convert MOP problems into single optimization problems (SOP). Almost all of the classical MOP methods imply quantitative as well as qualitative data. The assessment of social preference and social perception is a psychophysical problem dependent on the perception of environmental impact of the energy technologies. Almost all of our environmental and social criteria imply qualitative data, which needs to be quantified, as the assigned weights represent the social preferences and the priority of the environmental criteria, which should be optimized. Therefore a review of the scaling and weighting methods is required to be able to choose the best methods for the development of our sustainable energy mix optimization methodology.