Strategy improvement is a natural and well-studied family of algorithms for solving various classes of stochastic and deterministic graph games. We present an improved upper bound of O(n 2^n) on the number of iterations performed by the most natural, and most greedy, variant of the algorithm when applied to n-vertex Energy Games. We also obtain a similar upper bound of O(poly(n) · 2^n) on the expected number of iterations performed by Random-Edge, one of the most natural randomized variants of the algorithm. To the best of our knowledge, these are the first bounds for natural strategy-improvement algorithms on non-binary energy games that beat the trivial n^n = 2^{n log n} bound obtained by enumerating all strategies. The proof is based on a new adaptation of the layering technique of Dorfman, Kaplan, and Zwick (ICALP 2019).