The product development in mechanical engineering targets a multitude of design goals, e.g. mechanical performance, high quality, costs, low lead time and possibly many more. Additive Manufacturing (AM) can enable more design freedom than most fabrication processes and thus can facilitate better solutions. However, there is the need to take aim at sustainability-oriented criteria, and if possible already in the early design phases, where the effect of improvements is greatest. Especially regarding sustainability properties, often counterintuitive results are experienced, and in particular for relatively new technologies like AM. In conventional engineering, the solution is found by an experienced and well trained engineer who creates the (Computer-aided) design. However, with increasing complexity and interdependency of design variables, it can become very challenging to deal with the associated criteria in a coherent way. Generative Engineering with topology optimization can enable the efficient creation of designs and effective evolution regarding the different objectives and boundary conditions. It is an algorithmic design approach that automates many development processes by using simulation and optimization methods. Especially for additive manufacturing, this can make the exploitation of the added design freedom possible. The topology optimization as a mathematical optimization method relies on accurate modeling and simulation for generating fruitful solutions. That means that all the boundary conditions and objectives need to be formulated precisely. However, sustainability-oriented criteria are often hard to model in early design phases, without the basic properties like the material and geometrical features already being predefined. In addition, topology optimization commonly generates a unique solution for the set parameters. However, for a multi-objective product development, many solution alternatives would be highly appreciated to enable the effective balancing of the different criteria incorporating the preferences of the designer. The authors established a generative engineering workflow which incorporates a predictive Life Cycle Assessment (LCA) model into the topology optimization. Different sustainability-oriented measures are evaluated in the LCA on basis of intermediate topology results with modeling a simplified product system and representative processes of fabrication and use phase. The in- and output flows of the respective resources and their ecological footprint are evaluated and the next topology optimization iteration is informed in a clever way to generate an optimized solution. In addition, the topology optimization is advanced for generating a variety of topology results to the same design problem. In doing so, a multi-objective optimization is facilitated with coherently targeting multiple objectives. With the presented approach, the sustainability-oriented criteria, including the climate change potential and other ecological impact categories, can be incorporated already in the early design phase.