The Minimum Convex Partition problem (MCP) is a problem in which a point-set is used as the vertices for a planar subdivision, whose number of edges is to be minimized. In this planar subdivision, the outer face is the convex hull of the point-set, and the interior faces are convex. In this paper, we discuss and implement the approach to this problem using randomized local search, and different initialization techniques based on maximizing collinearity. We also solve small instances optimally using a SAT formulation. We explored this as part of the 2020 Computational Geometry Challenge, where we placed first as Team UBC.