First order saddle points have important applications in different fields of science and engineering. Some of their interesting applications include estimation of chemical reaction rate, image segmentation, path-planning and robotics navigation. Finding such points using evolutionary algorithms is a field that remains yet to be well investigated. In this paper, we present an evolutionary algorithm that is designed for finding multiple saddle points. In contrast to earlier work [1], we propose a new fitness function that favors 1st order saddle points or transition states. In particular, a valley adaptive clearing multi-modal evolutionary optimization approach is proposed to locate and archive multiple solutions by directing the search towards unexplored regions of the search space [2]. Experimental results on benchmark functions and the Lennard Jones Potential are presented to demonstrate the efficacy of the proposed algorithm in locating multiple 1st order saddle points.