Manufacturing processes are increasingly adapted to multi-material part production to facilitate lightweight design via improvement of structural performance. The difficulty lies in determining the optimum spatial distribution of the individual materials. Multi-Phase Topology Optimization (MPTO) achieves this aim via iterative, linear-elastic Finite Element (FE) simulations providing element- and part-level strain energy data under a given design load and using it to redistribute predefined material fractions to minimize total strain energy. The result us a part configuration offering maximum stiffness. The present study implements different material redistribution and optimization techniques and compares them in terms of optimization results and performance: Genetic algorithms are matched against simulated annealing, the latter with and without physics-based constraints. Both types employ partial randomization in generating new configurations to avoid settling into local rather than global minima of the objective function. This allows exploring a larger fraction of the full search space than accessed by classic gradient-based algorithms. Evaluation of the objective function depends on FE simulation, a computationally intensive task. Minimizing the required number of simulation runs is the task of the aforementioned constraints. The methodology is validated via a three point bending test scenario.