The economic optimization of a distillation column involves the selection of the number of trays and the feed- and side-streams locations, as well as the operating conditions that minimize the total investment and operation cost. In this paper, we present a superstructure-based optimization algorithm that combines the capabilities of commercial process simulators-taking advantage of the tailored algorithms designed for distillation and property estimation implemented in these simulators-and generalized disjunctive programming (GDP). The proposed algorithm iterates between two types of subproblems: A nonlinear programming (NLP) subproblem, in which the trays are divided into existing and nonexisting (nonexisting trays behave like simple bypasses without mass or heat exchange through the use of Murphree efficiencies), and a specially tailored master mixed integer linear programming (MILP) problem. The NLP subproblems are solved by integrating the process simulator with an external NLP solver. Several examples are presented which show promising results.