Recently, a new approach for optimization of Conditional Value-at-Risk CVaR was suggested and tested with several applications. By de nition, CVaR, also called Mean Excess Loss, Mean Shortfall or Tail VaR, is the expected loss exceeding Value-at Risk VaR. Central to the approach is an optimization technique for calculating VaR and optimizing CVaR simultaneously. This paper extends this approach to the optimization problems with CVaR constraints. In particular, the approach is used for nance applications such as maximizing returns under CVaR constraints. A case study for the portfolio of S&P 100 stocks is performed to demonstrate how the new optimization techniques can be implemented. Historical data were used for scenario generation.