In this paper we describe a randomized algorithm which returns a maximal spanning forest of an unknown {\em weighted} undirected graph making queries in expectation. For weighted graphs, this is optimal due to a result in [Auza and Lee, 2021] which shows an lower bound for zero-error randomized algorithms. These questions have been extensively studied in the past few years, especially due to the problem’s connections to symmetric submodular function minimization. We also describe a simple polynomial time deterministic algorithm that makes queries on undirected unweighted graphs and returns a maximal spanning forest, thereby (slightly) improving upon the state-of-the-art.