A new algorithmic framework for the solution of nonlinear chance-constrained optimization problems is proposed. Similar to Fletcher's S$l_1$QP algorithm, the method computes trial points from the minimization of a piece-wise quadratic model subject to a trust region. Theoretical convergence results and numerical experiments will be presented.