I will present a direct constrained minimization (DCM) algorithm for solving the Kohn-Sham equations. The key ingredients of this algorithm involve projecting the Kohn-Sham total energy functional into a sequences of subspaces of small dimensions and seeking the minimizer of total energy functional within each subspace. The minimizer of a subspace energy functional not only provides a search direction along which the KS total energy functional decreases but also gives an optimal ``step-length" to move along this search direction. I will provide some numerical examples to demonstrate the efficiency and accuracy of this approach and compare it with the widely used method of self-consistent field (SCF) iteration. I will also discuss a few other numerical issues in algorithms designed to solve the Kohn-Sham equations.