In the field of air conditioning control for office, improving office worker's thermal comfort with lower energy is required. Usually, single-objective mathematical optimization techniques are used for air conditioning control considering thermal comfort and energy saving. In such techniques, scalarization method (e.g. linearly weighting addition, constraint transformation) is used. However, it is difficult to match dynamic changes of temperature and/or consumed power requirements in such methods. In this study, we apply an evolutionary multi-objective optimization (EMO) method to decide optimal operation of an air conditioning system considering both thermal comfort and power consumption. We formulate two objective functions, thermal comfort and power consumption. Predicted Mean Vote (PMV) was used as the first objective function. PMV is widely known thermal comfort index of indoor conditions adopted as an ISO standard. An estimation method of power consumption of air conditioning from a heat load was used as the second objective function. The heat load is amount of heat provided or removed by an air conditioning system. In this study, we used OMOPSO (Optimized Multi-Objective Particle Swarm Optimization), one of the MOPSO algorithms, which uses an external archive and mutation operators. An archive of OMOPSO has a concept of ε-dominance, so in our study we varied parameter ε to finding better solutions. Comparison with constant temperature control, our results demonstrate the EMO method is effective to get a set of solutions for the best considering trade-off between thermal comfort and power consumption. By not using ε-dominance, we got wide range of PMV value recommended by ISO standard. After searching pareto solutions, if we can select an effectual solution adopting to requirements, optimal air conditioner control considering requirement is realizable.