Despite the importance of selection for quality characteristics in plant improvement programmes, literature on experimental design and statistical analysis for these traits is scarce. Most quality traits are obtained from multi-phase experiments in which plant varieties are first grown in a field trial then further processed in the laboratory. In the present paper a general mixed model approach for the analysis of multi-phase data is described, with particular emphasis on quality trait data that are often highly unbalanced and involve substantial sources of non-genetic variation and correlation. Also detailed is a new approach for experimental design that employs partial replication in all phases. The motivation for this was the high cost of obtaining quality trait data, thus the need to limit the total number of samples tested, but still allow use of the mixed model analysis. A simulation study is used to show that the combined use of the new designs and mixed model analysis has substantial benefits in terms of the genetic gain from selection.