We investigate the combinatorial effect of evolutionary multi-objective optimization (EMO) with interactive evolutionary computation (IEC). The purposes and combination ways of several presented EMO and IEC researches are different. We evaluated seven combination ways of four EMO objectives given by fitness functions and one IEC objective given by a pseudo-IEC user outputting stable evaluation regardless repeated experiments in our previous experiments. In this paper, we extend experimental conditions to 39 and evaluate them: 3 pseudo-users X 13 combination ways of 4 + 1 objectives. We also consider features of this system.