We present a novel approach based on genetic algorithms for performing camera calibration. Contrary to the classical nonlinear photogrammetric approach, the proposed technique can correctly find the near-optimal solution without the need of initial guesses (with only very loose parameter bounds) and with a minimum number of control points (7 points). Results from our extensive study using both synthetic and real image data as well as performance comparison with Tsai's procedure demonstrate the excellent performance of the proposed technique in terms of convergence, accuracy, and robustness.