This study presents development of a new approach involving adaptable chemotactic step size in Bacterial Foraging Algorithm (BFA). Standard BFA only offers a constant chemotactic step size for all nutrient values. The chemotactic step size can be made adaptive, i.e., the chemotactic step size is changed in a certain manner. The objective of the study is to investigate adaptation schemes in the BFA so that the chemotactic step size may change depending on the nutrient value. The adaptation mechanism is made by incorporating nutrient value of every bacterium into three functions, namely linear function, quadratic function and exponential function and by using a fuzzy adaptation scheme. In the full BFA algorithm, the proposed approach will be used as vary the chemotactic step size. Test results with benchmark functions show that BFA with the proposed adaptable chemotactic step size is able to converge faster to the global optimum and to achieve better optimum value than that achieved by standard BFA.