Summary: A new successive quadratic programming type feasible algorithm is proposed for the minimization of a continuously differentiable function subject to nonlinear inequality constraints. The algorithm only needs to solve one quadratic programming subproblem per iteration and can produce feasible successive iterates which have global convergence and superlinear convergence under some suitable assumptions.