In this paper, we describe an evolutionary approach to one of the most challenging problems in computer music: modeling the knowledge applied by a musician when performing a score of a piece in order to produce an expressive performance of the piece. We extract a set of acoustic features from Jazz recordings thereby providing a symbolic representation of the musician's expressive performance. By applying a sequential covering evolutionary algorithm to the symbolic representation, we obtain an expressive performance computational model capable of endowing a computer generated music performance with the timing and energy expressiveness that characterizes human generated music.