The Motif finding problem is important in bioinformatics, aiming to find recurring sequences in biological data. These motifs, are important for understanding how genes work, how proteins function, and how cells operate. Accurately detecting these patterns is essential for understanding of biology, aiding in scientific research, and developing treatments for diseases. Addressing the motif finding problem efficiently across heterogeneous CPU architectures presents significant challenges in computational efficiency and resource utilization. The variation in the number and speed of cores across CPUs requires developing scheduling strategies to efficiently distribute workloads among these architectures. This paper presents an efficient strategy for optimizing task distribution across heterogeneous CPU architectures. The proposed approach makes performance enhancement with 9 % solving Motif finding problem in CPU heterogeneous architectures. This improvement significantly speed up the process of identifying important biological patterns, making bioinformatics research quicker and more cost-effective. In addition, it has a significant impact on enhancing computational efficiency and reducing costs in high-performance computing environments.