In the literature on runtime analyses of estimation of distribution algorithms (EDAs), researchers have recently explored univariate EDAs for multi-valued decision variables. Particularly, Jedidia et al. gave the first runtime analysis of the multi-valued UMDA on the r-valued LeadingOnes ( \(r\) -LeadingOnes) functions and Adak and Witt gave the first runtime analysis of the multi-valued cGA ( \(r\) -cGA) on the r-valued OneMax function. We utilize their framework to conduct an analysis of the multi-valued cGA on the r-valued LeadingOnes function. Even for the binary case, a runtime analysis of the classical cGA on LeadingOnes was not yet available. In this work, we show that the runtime of the \(r\) -cGA on \(r\) -LeadingOnes is \(\textrm{O}(n^2 r^2\log ^3 n\log ^2 r)\) with high probability.