Two-colour microarrays are used to study differential gene expression on a large scale. Experimental planning can help reduce the chances of wrong inferences about whether genes are differentially expressed. Previous research on this problem has focused on minimising estimation errors (according to variance-based criteria such as A-optimality) on the basis of optimistic assumptions about the system studied. In this paper, we propose a novel planning criterion to evaluate existing plans for microarray experiments. The proposed criterion is 'Generalised-A Optimality' that is based on realistic assumptions that include bias errors. Using Generalised-A Optimality, the reference-design approach is likely to yield greater estimation accuracy in specific situations in which loop designs had previously seemed superior. However, hybrid designs are likely to offer higher estimation accuracy than reference, loop and interwoven designs having the same number of samples and slides. These findings are supported by data from both simulated and real microarray experiments.