1. A method to optimize a multi-parameter design (MPD) having design variables and performance metrics, the method comprising steps of: a) calculating, at a first set of design corners, by using a computer, a performance value of each performance metric for each candidate design of a first set of candidate designs, each performance metric being a function of at least one of the design variables, the design variables defining a design variables space, the MPD having associated thereto the first set of candidate designs, random variables, defining a random variables space, and environmental variables, defining an environmental variables space, the random variables space and the environmental variables space defining design corners at which the candidate designs can be evaluated, each candidate design representing a combination of design variables; b) calculating, for each performance value, by using the computer, a performance value uncertainty, the performance value of each performance metric for each candidate design of the first set of candidate designs, and its respective performance value uncertainty, defining a first set of data; c) in accordance with the first set of data, building a model of each performance metric to obtain a first set of models, each model mapping at least one design variable to a model output and to a model output uncertainty; d) storing the first set of models in a characterization database; e) displaying, for selection, one or more models of the first set of models, and their model uncertainty; f) in response to a selection of one or more candidate designs, which defines selected candidate designs, and in accordance with the one or more displayed models: i) adding the selected candidate designs to the first set of candidate designs, to obtain a second set of candidate designs; ii) calculating, at a second set of design corners, a performance value, and a performance value uncertainty, for each performance metric of each selected candidate design; iii) adding the performance value and the performance value uncertainty of the selected candidate designs to the first set of data, to obtain a second set of data; iv) in accordance with the second set of data, modifying the model of each performance metric, to obtain a second set of models, each having a modified model uncertainty; v) displaying, for inspection, one or more models of the second set of models; vi) in accordance with the second set of candidate designs, and in accordance with pre-determined search rules, generating additional candidate designs by performing a search of the design variables space, the search being biased towards optimality and uncertainty of the performance metrics; vii) adding the additional candidate designs to the second set of candidate designs, to obtain a third set of candidate designs; viii) calculating, at a third set of design corners, a performance value, and a performance value uncertainty, for each performance metric of each additional candidate design; ix) adding the performance value and the performance value uncertainty of the additional candidate designs to the second set of data, to obtain a third set of data; and x) in accordance with the third set of data, modifying the model of each performance metric and the model uncertainty of each model, to obtain a third set models; and g) displaying, for inspection, one or more models of the third set of models.