Pitfalls of using a single criterion for selecting experimental designs

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An algorithm for fast optimal Latin hypercube design of experiments (2010, background): “There exist many measures for the quality of fit, depending on the particular problem under consideration [14, 15].”
A comparative study of metamodeling methods considering sample quality merits (2010, background): “A recent study also shows that it is risky to select the design of experiments based on a single measure ( Goel et al. 2008 ).”
Comparing error estimation measures for polynomial and kriging approximation of noise-free functions (2009, background): “Goel et al. (2008) demonstrated that a combination of the Latin hypercube sampling (LHS) and the D-optimality criterion to construct experimental designs is helpful in reducing noise and bias errors simultaneously.”