To address the challenges posed by constrained mixed-integer spaces in experimental planning, in this work, we propose a different framework -the use of mixed-integer surrogates and acquisition functions, for which we adopt the recently developed optimization package, Piecewise Affine Surrogate-based optimization (PWAS). PWAS is selected as it is tailored to address linearly-constrained discrete and mixed-variable domains enabling direct incorporation of discrete/categorical decision variables in the optimization process, which provides a more realistic representation of the problem. Additionally, PWAS can handle mixed-variable linear equality/inequality constraints, which are commonly encountered in physical and chemical systems, ensuring the proposal of feasible solutions throughout the design procedure.