In this study, the Genetic Programming (GP) based meta-modeling method was employed to predict and formulate the properties of 3D printed Polycaprolactone/Nano-Hydroxyapatite/Chitin-Nano-Whisker nanocomposite bone tissue scaffolds with Gyroid design and generate multiple objective functions. Meta-modeling was applied to the extracted experimental and numerical characterization results of the scaffolds as a process of data-driven modeling. The assessed properties are compressive strength, average apparent modulus, compressive strength after four months of biodegradation, average apparent modulus after four months of biodegradation, biodegradation percentage after six months, and bone cells proliferation. The obtained objective functions by meta-modeling are optimized using the Non-dominated Sorting Genetic Algorithm III (NSGA-III). The optimized non-dominated candidate solutions (i.e., Pareto-front optimal solutions) of the nanocomposite bone tissue scaffolds are reported with the Nano-filler percentages under 3%, and the Gyroid design porosity ranged in [60%, 90%]. This study proposes a method to achieve a set of optimized solutions to select the best functional properties of bone tissue scaffolds based on the specific application or the required porosity. Also, this method helps to reduce the number of expensive and time-consuming experiments significantly.