In this paper, an improvement of the quantization optimization algorithm for the MPEG Advanced Audio Coder (AAC) is presented. This algorithm, given a bit-rate constraint, minimizes the perceived distortion generated by the signal compression. The distortion can be related to the quantization error level over frequency sub-bands through an auditory model. Thus, optimizing the quantification requires knowledge of the rate-distortion function for each sub-band. When this function can be modeled in a simple way, the algorithm can take a one-loop recursive structure. However, in the MPEG AAC, the rate-distortion function is hard to characterize, since AAC makes use of non-linear quantizers and variable length entropy coders. As a result, the standard algorithm makes use of two nested loops with a local decoder, in order to measure the error level rather than predicting its value. We first describe a partial sub-band modeling of the rate-distortion function of interest in the MPEG AAC. Then, using a statistical approach, we find a relationship between the error level and the so-called quantization ``scale-factor'' and propose a new algorithm that is basically similar to a classical one loop ``bit allocation'' process. Finally, we describe the complete algorithm and show that it is more efficient than the standard one.