The main goal of microarray experiments is to select a small subset of genes that are differentially expressed among competing mRNA samples. For a given set of such mRNA samples, it is possible to consider a number of two-color cDNA microarray designs with a fixed number of arrays. Appropriate criteria can be used to select an efficient design from such a set of alternative experimental designs. In practice, however, microarray expression data often contain missing observations and the most efficient design (with complete observations) for a specific setup may not be efficient in the presence of missing observations. In this article, we propose two criteria to address the robustness of microarray designs against missing observations. We demonstrate the simultaneous use of efficiency and robustness criteria to select good microarray designs for both one-factor and multi-factor experiments.