In this paper we consider nonlinear constrained optimization problems in case where the ﬁrst order derivatives of the objective function and the constraints can not be used. Up to date only a few approaches have been proposed for tackling such a class of problems. In this work we propose a new algorithm. The starting point of the proposed approach is the possibility to transform the original constrained problem into an unconstrained or linearly constrained minimization of a nonsmooth ex-act penalty function. In this paper we propose a derivative-free algorithm which overcomes the preceding diﬃculties and produces a sequence of points that admits a subsequence converging towards Karush-Kuhn-Tucker points of the constrained problem. Numerical results on a set of test problems are reported which show the viability of the proposed algorithm. Giampaolo Liuzzi è docente presso la Facoltà di Ingegneria della “Sapienza” Università di Roma.Stefano Lucidi è docente presso la Facoltà di Ingegneria della “Sapienza” Università di Roma.