In this paper, we propose to develop an intelligent camera control algorithm for scientific visualization. Intelligent camera control refers to a path planning algorithm that allows a virtual camera to navigate a scene autonomously. Intelligent camera overcomes some shortcomings of traditional manual navigation such as the risk of getting lost in the scene, or the user's distraction from the main goal of the study. In the past years, several path planning approaches have been proposed. While those approaches focus on determining the shortest path between two points, they cannot adapt to multiple constraints that a virtual camera is subjected to, in scientific visualization. Inspired by Unmanned Aerial Vehicle path planning, our algorithm uses genetic algorithm as an optimization tool. Finally, the paper presents the experimental results of our algorithm including an empirical study to determine the optimal values for the genetic parameters. © 2011 Springer-Verlag.