Spatial branching, i.e., branching on continuous variables, is a necessary tool of global nonlinear solvers for tackling non-convexities arising in the objective function and/or the constraints. Spatial branching is generally trickier than branching in the mixed integer linear world and it has been comparably less studied than its integer counterpart. Similar to other techniques that were originally intended for MINLP but are now common in the MIP world, we show that it can be successfully used on a special class of mixed integer linear optimization problems, where it is more effective than the standard integer branching machinery of a commercial MIP solver.