In this paper, a new optimization technique called SOFT(self-organizing fuzzy technique) is proposed to solve the macro-cell placement problem. In SOFT, different criteria are simultaneously accounted by a novel fuzzy gain function which models expert knowledge to control the optimization process. The presented procedure is an adaptation of Kohonen's self-organization algorithm which is well suited for implementation on massively parallel architecture for fast computing. The MCNC benchmark examples are presented to verify the performance and feasibility of SOFT. Comparisons are made with the Hopfield network, SOAP and TimberWolf MC5.6. Experiments show that the proposed method yields an average of 17% improvement in total wire length compared with previous methods. Large size problems with 225 and 1024 arbitrarily-sized macrocells are also presented.