Interest in the genomics of Eucalyptus has skyrocketed thanks to the recent sequencing of the genome of Eucalyptus grandis and to a growing number of large-scale transcriptomic studies. Quantitative reverse transcription–PCR (RT–PCR) is the method of choice for gene expression analysis and can now also be used as a high-throughput method. The selection of appropriate internal controls is becoming of utmost importance to ensure accurate expression results in Eucalyptus. To this end, we selected 21 candidate reference genes and used high-throughput microfluidic dynamic arrays to assess their expression among a large panel of developmental and environmental conditions with a special focus on wood-forming tissues. We analyzed the expression stability of these genes by using three distinct statistical algorithms (geNorm, NormFinder and ΔCt), and used principal component analysis to compare methods and rankings. We showed that the most stable genes identified depended not only on the panel of biological samples considered but also on the statistical method used. We then developed a comprehensive integration of the rankings generated by the three methods and identified the optimal reference genes for 17 distinct experimental sets covering 13 organs and tissues, as well as various developmental and environmental conditions. The expression patterns of Eucalyptus master genes EgMYB1 and EgMYB2 experimentally validated our selection. Our findings provide an important resource for the selection of appropriate reference genes for accurate and reliable normalization of gene expression data in the organs and tissues of Eucalyptus trees grown in a range of conditions including abiotic stresses.