A method is given for maximizing a linear function subject to a quadratic and a number of linear constraints. The method differs from general convex programming methods by terminating in a finite number of iterations and is actually an application of the Simplex and dual methods for quadratic programming to parametric quadratic programming problems. The method is shown to be useful for the solution of some chance-constrained programming problems. Detailed rules and a simple example of an application are given.