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Optimization and expansion are two modes of staged evolution of complex systems where macroscopic observables change at a decreasing, respectively increasing, rate. A prime example of evolutionary expansion, Gross Domestic Product (GDP) time series gauge economic activities in changing societal structures,} and the accelerating trend of their growth probably reflects a manyfold increase of the human interactions that drive change. We show how optimization and expansion can coexist by replacing `wall clock time' $t$ as independent variable with a measure of human interactions intensity $τ$. Our analysis of eight centuries of yearly GDP data from three regions of Western Europe is carried out in two steps. First, a Monte Carlo algorithm is used to fit the GDP data to a piecewise continuous function comprising a sequence of exponentials with different exponents. In a second step, GDP data are plotted vs. $τ$ and shown to display two logarithmic regimes, both decelerating, that are joined by a power-law cross-over period. We connect the end of the first regime and the beginning of the second with the dawn of the Industrial Revolution and the societal impact of new transport, communication and production technologies that became widely available after World War I. We conclude that wealth evolution in terms of $τ$ is a decelerating process with the hallmarks of record dynamics optimization.