This paper compares the criteria for updating the Kriging response surface models in multi-objective optimization: expected improvement (EI), expected hypervolume improvement (EHVI), estimation (EST), and those combination (EHVI+EST). EI has been conventionally used as the criterion considering the stochastic improvement of each objective function value individually, while EHVI has been recently proposed as the criterion considering the stochastic improvement of the front of non-dominated solutions in multi-objective optimization. EST is the value of each objective function, which is estimated non-stochastically by the Kriging model without considering its uncertainties. Numerical experiments were implemented in the welded beam design problem, and empirically showed that, in a non-constrained case, EHVI keeps a balance between accurate, wide, and uniform search for non-dominated solutions on the Kriging models in multi-objective optimization. In addition, the present experiments suggested future investigation into the techniques for handling constraints with uncertainties to enhance the capability of EHVI in a constrained case.