We show that conjoint analysis, a popular multi-attribute preference assessment technique used in market research, is a well suited tool to simultaneously evaluate a multitude of gamut mapping algorithms with a psycho-visual testing load not much higher than in conventional psycho-visual tests of gamut mapping algorithms. The gamut mapping algorithms that we test using conjoint analysis are derived from a master algorithm by choosing different parameter settings. Simultaneously we can also test the influence of additional parameters like gamut size on the perceived quality of a mapping. Conjoint analysis allows us to quantify the contribution of every single parameter value to the perceived quality.