Path planning is a crucial issue in unknown environments where an autonomous mobile agent has to reach a particular destination from some initial location. There are several incremental algorithms such as D-star, D-star Lite  that are able to ensure reasonable paths in terms of path length in unknown environments. However, in many real-world problems we realize that path length is not only the sole objective. For example in computer games, a non-player character needs to not only find a minimum cost path to some target location but also minimize threat exposure. This means that path planning/finding activity of an agent in a multi-agent environment has to consider more than one objective to be achieved. In this paper, we propose a new incremental search algorithm called MOD star Lite extending Koenig's D-star Lite algorithm and show that MOD* Lite is able to optimize path quality in more than one criteria that cannot be transformed to each other. Experimental results show that MOD* Lite is able to find optimal solutions and is fast enough to be used in real-world multi-agent applications such as robotics, computer games, or virtual simulations.