This paper investigates multi-static position estimation in cell-free massive multiple-input multiple-output (CF mMIMO) architectures, where orthogonal time frequency space (OTFS) is used as an integrated sensing and communication (ISAC) signal. A maximum likelihood position estimation scheme is proposed, where the required search space is reduced by employing a common reference system. Closed-form expressions for the Cramér-Rao lower bound and the position error bound (PEB) in multi-static position estimation are derived, providing quantitative evaluations of sensing performance. These theoretical bounds are further generalized into a universal structure to support other ISAC signals. To enhance overall system performance and adapt to dynamic network requirements, a joint AP operation mode selection and power allocation algorithm is developed to maximize the minimum user communication spectral efficiency (SE) while ensuring a specified sensing PEB requirement. Moreover, a decomposition method is introduced to achieve a better tradeoff between complexity and ISAC performance. The results verify the effectiveness of the proposed algorithms, demonstrating the superiority of the OTFS signal through a nearly twofold SE gain over the orthogonal frequency division multiplexing (OFDM) signal. These findings highlight promising advantages of the CF-ISAC systems from a novel parameter estimation perspective, particularly in high-mobility vehicle-to-everything applications.