Conventional techniques for optimal experimental design are based on local sensitivity analysis to quantify parameter effects on the output. However, one of the key challenges for experimental design is that the local sensitivity is dependent on the unknown parameter values for a nonlinear model. This problem can be addressed if the sensitivity matrix, used in experimental design, could be computed by global sensitivity analysis techniques rather than local sensitivity analysis methods. However, not all existing global sensitivity analysis measures can compute such a sensitivity matrix. This paper presents a necessary condition for integrating global sensitivity analysis with experimental design criteria, i.e., the design criterion of the global sensitivity matrix reduces to the one applied to the local sensitivity matrix if the parameter uncertainty range tends to zero. Four different sensitivity measures are analyzed using this condition and the results are illustrated in a detailed case study where a comparison with local design and Bayesian design is made. (c) 2012 Elsevier Ltd. All rights reserved.