This paper is a continuation of a previous one which investigates programming over a Markov-renewal process—in which the intervals between transitions of a system from state i to state j are independent samples from a distribution that may depend upon both i and j. Given a reward structure, and a decision mechanism that influences both the rewards and the Markov-renewal process, the problem is to select alternatives at each transition so as to maximize total expected reward. The first portion of the paper investigated various finite-return models. In this part of the paper, we investigate the infinite-return models, where it becomes necessary to consider only stationary policies that maximize the dominant term in the reward. It is then important to specify whether the limiting experiment is (I) undiscounted, with the number of transitions n → ∞, (II) undiscounted, with a time horizon t → ∞, or (III) discounted, infinite n or t, with discount factor a → 0. In each case, a limiting form for the total expected reward is shown, and an algorithm developed to maximize the rate of return. The problem of finding the optimal or near-optimal policies in the case of ties is still computationally unresolved. Extensions to nonergodic processes are indicated, and special results for the two-state process are presented. Finally, an example of machine maintenance and repair is used to illustrate the generality of the models and the special problems that may arise.