Given a discrete source distribution $μ$ and discrete target distribution $ν$ on a common finite state space $\mathcal{X}$, we are tasked with transporting $μ$ to $ν$ using a given discrete-time Markov chain $X$ with the quickest possible time on average. We define the optimal transport time $H(μ,ν)$ as stopping rule of $X$ that gives the minimial expected transport time. This is also known as the access time from $μ$ to $ν$ of $X$ in [L. Lovász and P. Winkler. Efficient Stopping Rules for Markov Chains. Proceedings of the Twenty-seventh Annual ACM Symposium on Theory of Computing (STOC '95) 76-82.]. We study bounds of $H(μ,ν)$ in various special graphs, which are expressed in terms of the mean hitting times of $X$ as well as parameters of $μ$ and $ν$ such as their moments. Among the Markov chains that we study, random walks on complete graphs is a good choice for transport as $H(μ,ν)$ grows linearly in $n$, the size of the state space, while that of the winning streak Markov chain exhibits exponential dependence in $n$.