In this paper we look at the experimental design for multi-objective problems, where the objectives can be evaluated independently (decoupled) and thus it may make sense to evaluate different solutions for each objective if the objectives have different evaluation costs and/or different landscape characteristics. We propose to iteratively add design points in a way that minimises the total integrated mean squared prediction error assuming a Gaussian process response surface model, and show that allowing decoupled evaluations can lead to significantly better Pareto front estimations than a coupled design of experiments if the evaluation costs of the objectives are different. We also find that our approach of minimising mean squared prediction error yields significantly better results than standard Latin Hypercube designs even if the evaluation costs and landscape characteristics of the objectives are the same.