Several indicator-based evolutionary multiobjective optimization algorithms have been proposed in the literature. The notion of optimal mu-distributions formalizes the optimization goal of such algorithms: find a set of it solutions that maximizes the underlying indicator among all sets with it, solutions. In particular for the often used hypervolume indicator, optimal it-distributions have been theoretically analyzed recently. All those results, however, cope with bi-objective problems only. It is the main goal of this paper to extend some of the results to the 3-objective case. This generalization is shown to be not straight-forward as a solution's hypervolume contribution has not a simple geometric shape anymore in opposition to the hi-objective case where it is always rectangular. In addition, we investigate the influence of the reference point on optimal mu-distributions and prove that also in the 3-objective case situations exist for which the Pareto front's extreme points cannot be guaranteed in optimal mu-distributions.