We propose a new framework that operates on molecular graphs and uses the well-known multi-objective optimization algorithms NSGA-II and NSGA-III to optimize multiple, eventually conflicting, objective functions in an unconstrained chemical space. Working with the cheminformatics library RDKit, We are developing new mutation and crossover operators in combination with sets of functional groups and rings from the literature to parse the drug-like chemical space efficiently.